R/sagemaker_operations.R

Defines functions sagemaker_update_domain sagemaker_update_devices sagemaker_update_device_fleet sagemaker_update_context sagemaker_update_code_repository sagemaker_update_artifact sagemaker_update_app_image_config sagemaker_update_action sagemaker_stop_transform_job sagemaker_stop_training_job sagemaker_stop_processing_job sagemaker_stop_pipeline_execution sagemaker_stop_notebook_instance sagemaker_stop_monitoring_schedule sagemaker_stop_labeling_job sagemaker_stop_inference_recommendations_job sagemaker_stop_inference_experiment sagemaker_stop_hyper_parameter_tuning_job sagemaker_stop_edge_packaging_job sagemaker_stop_edge_deployment_stage sagemaker_stop_compilation_job sagemaker_stop_auto_ml_job sagemaker_start_pipeline_execution sagemaker_start_notebook_instance sagemaker_start_monitoring_schedule sagemaker_start_inference_experiment sagemaker_start_edge_deployment_stage sagemaker_send_pipeline_execution_step_success sagemaker_send_pipeline_execution_step_failure sagemaker_search sagemaker_retry_pipeline_execution sagemaker_render_ui_template sagemaker_register_devices sagemaker_query_lineage sagemaker_put_model_package_group_policy sagemaker_list_workteams sagemaker_list_workforces sagemaker_list_user_profiles sagemaker_list_trials sagemaker_list_trial_components sagemaker_list_transform_jobs sagemaker_list_training_jobs_for_hyper_parameter_tuning_job sagemaker_list_training_jobs sagemaker_list_tags sagemaker_list_subscribed_workteams sagemaker_list_studio_lifecycle_configs sagemaker_list_stage_devices sagemaker_list_spaces sagemaker_list_resource_catalogs sagemaker_list_projects sagemaker_list_processing_jobs sagemaker_list_pipelines sagemaker_list_pipeline_parameters_for_execution sagemaker_list_pipeline_executions sagemaker_list_pipeline_execution_steps sagemaker_list_notebook_instances sagemaker_list_notebook_instance_lifecycle_configs sagemaker_list_monitoring_schedules sagemaker_list_monitoring_executions sagemaker_list_monitoring_alerts sagemaker_list_monitoring_alert_history sagemaker_list_models sagemaker_list_model_quality_job_definitions sagemaker_list_model_packages sagemaker_list_model_package_groups sagemaker_list_model_metadata sagemaker_list_model_explainability_job_definitions sagemaker_list_model_cards sagemaker_list_model_card_versions sagemaker_list_model_card_export_jobs sagemaker_list_model_bias_job_definitions sagemaker_list_lineage_groups sagemaker_list_labeling_jobs_for_workteam sagemaker_list_labeling_jobs sagemaker_list_inference_recommendations_jobs sagemaker_list_inference_recommendations_job_steps sagemaker_list_inference_experiments sagemaker_list_images sagemaker_list_image_versions sagemaker_list_hyper_parameter_tuning_jobs sagemaker_list_human_task_uis sagemaker_list_hubs sagemaker_list_hub_contents sagemaker_list_hub_content_versions sagemaker_list_flow_definitions sagemaker_list_feature_groups sagemaker_list_experiments sagemaker_list_endpoints sagemaker_list_endpoint_configs sagemaker_list_edge_packaging_jobs sagemaker_list_edge_deployment_plans sagemaker_list_domains sagemaker_list_devices sagemaker_list_device_fleets sagemaker_list_data_quality_job_definitions sagemaker_list_contexts sagemaker_list_compilation_jobs sagemaker_list_code_repositories sagemaker_list_candidates_for_auto_ml_job sagemaker_list_auto_ml_jobs sagemaker_list_associations sagemaker_list_artifacts sagemaker_list_apps sagemaker_list_app_image_configs sagemaker_list_aliases sagemaker_list_algorithms sagemaker_list_actions sagemaker_import_hub_content sagemaker_get_search_suggestions sagemaker_get_scaling_configuration_recommendation sagemaker_get_sagemaker_servicecatalog_portfolio_status sagemaker_get_model_package_group_policy sagemaker_get_lineage_group_policy sagemaker_get_device_fleet_report sagemaker_enable_sagemaker_servicecatalog_portfolio sagemaker_disassociate_trial_component sagemaker_disable_sagemaker_servicecatalog_portfolio sagemaker_describe_workteam sagemaker_describe_workforce sagemaker_describe_user_profile sagemaker_describe_trial_component sagemaker_describe_trial sagemaker_describe_transform_job sagemaker_describe_training_job sagemaker_describe_subscribed_workteam sagemaker_describe_studio_lifecycle_config sagemaker_describe_space sagemaker_describe_project sagemaker_describe_processing_job sagemaker_describe_pipeline_execution sagemaker_describe_pipeline_definition_for_execution sagemaker_describe_pipeline sagemaker_describe_notebook_instance_lifecycle_config sagemaker_describe_notebook_instance sagemaker_describe_monitoring_schedule sagemaker_describe_model_quality_job_definition sagemaker_describe_model_package_group sagemaker_describe_model_package sagemaker_describe_model_explainability_job_definition sagemaker_describe_model_card_export_job sagemaker_describe_model_card sagemaker_describe_model_bias_job_definition sagemaker_describe_model sagemaker_describe_lineage_group sagemaker_describe_labeling_job sagemaker_describe_inference_recommendations_job sagemaker_describe_inference_experiment sagemaker_describe_image_version sagemaker_describe_image sagemaker_describe_hyper_parameter_tuning_job sagemaker_describe_human_task_ui sagemaker_describe_hub_content sagemaker_describe_hub sagemaker_describe_flow_definition sagemaker_describe_feature_metadata sagemaker_describe_feature_group sagemaker_describe_experiment sagemaker_describe_endpoint_config sagemaker_describe_endpoint sagemaker_describe_edge_packaging_job sagemaker_describe_edge_deployment_plan sagemaker_describe_domain sagemaker_describe_device_fleet sagemaker_describe_device sagemaker_describe_data_quality_job_definition sagemaker_describe_context sagemaker_describe_compilation_job sagemaker_describe_code_repository sagemaker_describe_auto_ml_job_v2 sagemaker_describe_auto_ml_job sagemaker_describe_artifact sagemaker_describe_app_image_config sagemaker_describe_app sagemaker_describe_algorithm sagemaker_describe_action sagemaker_deregister_devices sagemaker_delete_workteam sagemaker_delete_workforce sagemaker_delete_user_profile sagemaker_delete_trial_component sagemaker_delete_trial sagemaker_delete_tags sagemaker_delete_studio_lifecycle_config sagemaker_delete_space sagemaker_delete_project sagemaker_delete_pipeline sagemaker_delete_notebook_instance_lifecycle_config sagemaker_delete_notebook_instance sagemaker_delete_monitoring_schedule sagemaker_delete_model_quality_job_definition sagemaker_delete_model_package_group_policy sagemaker_delete_model_package_group sagemaker_delete_model_package sagemaker_delete_model_explainability_job_definition sagemaker_delete_model_card sagemaker_delete_model_bias_job_definition sagemaker_delete_model sagemaker_delete_inference_experiment sagemaker_delete_image_version sagemaker_delete_image sagemaker_delete_human_task_ui sagemaker_delete_hub_content sagemaker_delete_hub sagemaker_delete_flow_definition sagemaker_delete_feature_group sagemaker_delete_experiment sagemaker_delete_endpoint_config sagemaker_delete_endpoint sagemaker_delete_edge_deployment_stage sagemaker_delete_edge_deployment_plan sagemaker_delete_domain sagemaker_delete_device_fleet sagemaker_delete_data_quality_job_definition sagemaker_delete_context sagemaker_delete_code_repository sagemaker_delete_association sagemaker_delete_artifact sagemaker_delete_app_image_config sagemaker_delete_app sagemaker_delete_algorithm sagemaker_delete_action sagemaker_create_workteam sagemaker_create_workforce sagemaker_create_user_profile sagemaker_create_trial_component sagemaker_create_trial sagemaker_create_transform_job sagemaker_create_training_job sagemaker_create_studio_lifecycle_config sagemaker_create_space sagemaker_create_project sagemaker_create_processing_job sagemaker_create_presigned_notebook_instance_url sagemaker_create_presigned_domain_url sagemaker_create_pipeline sagemaker_create_notebook_instance_lifecycle_config sagemaker_create_notebook_instance sagemaker_update_workteam sagemaker_update_workforce sagemaker_update_user_profile sagemaker_update_trial_component sagemaker_update_trial sagemaker_update_training_job sagemaker_update_space sagemaker_update_project sagemaker_update_pipeline_execution sagemaker_update_pipeline sagemaker_update_notebook_instance_lifecycle_config sagemaker_update_notebook_instance sagemaker_update_monitoring_schedule sagemaker_update_monitoring_alert sagemaker_update_model_package sagemaker_update_model_card sagemaker_update_inference_experiment sagemaker_update_image_version sagemaker_update_image sagemaker_update_hub sagemaker_update_feature_metadata sagemaker_update_feature_group sagemaker_update_experiment sagemaker_update_endpoint_weights_and_capacities sagemaker_update_endpoint sagemaker_create_monitoring_schedule sagemaker_create_model_quality_job_definition sagemaker_create_model_package_group sagemaker_create_model_package sagemaker_create_model_explainability_job_definition sagemaker_create_model_card_export_job sagemaker_create_model_card sagemaker_create_model_bias_job_definition sagemaker_create_model sagemaker_create_labeling_job sagemaker_create_inference_recommendations_job sagemaker_create_inference_experiment sagemaker_create_image_version sagemaker_create_image sagemaker_create_hyper_parameter_tuning_job sagemaker_create_human_task_ui sagemaker_create_hub sagemaker_create_flow_definition sagemaker_create_feature_group sagemaker_create_experiment sagemaker_create_endpoint_config sagemaker_create_endpoint sagemaker_create_edge_packaging_job sagemaker_create_edge_deployment_stage sagemaker_create_edge_deployment_plan sagemaker_create_domain sagemaker_create_device_fleet sagemaker_create_data_quality_job_definition sagemaker_create_context sagemaker_create_compilation_job sagemaker_create_code_repository sagemaker_create_auto_ml_job_v2 sagemaker_create_auto_ml_job sagemaker_create_artifact sagemaker_create_app_image_config sagemaker_create_app sagemaker_create_algorithm sagemaker_create_action sagemaker_batch_describe_model_package sagemaker_associate_trial_component sagemaker_add_tags sagemaker_add_association

Documented in sagemaker_add_association sagemaker_add_tags sagemaker_associate_trial_component sagemaker_batch_describe_model_package sagemaker_create_action sagemaker_create_algorithm sagemaker_create_app sagemaker_create_app_image_config sagemaker_create_artifact sagemaker_create_auto_ml_job sagemaker_create_auto_ml_job_v2 sagemaker_create_code_repository sagemaker_create_compilation_job sagemaker_create_context sagemaker_create_data_quality_job_definition sagemaker_create_device_fleet sagemaker_create_domain sagemaker_create_edge_deployment_plan sagemaker_create_edge_deployment_stage sagemaker_create_edge_packaging_job sagemaker_create_endpoint sagemaker_create_endpoint_config sagemaker_create_experiment sagemaker_create_feature_group sagemaker_create_flow_definition sagemaker_create_hub sagemaker_create_human_task_ui sagemaker_create_hyper_parameter_tuning_job sagemaker_create_image sagemaker_create_image_version sagemaker_create_inference_experiment sagemaker_create_inference_recommendations_job sagemaker_create_labeling_job sagemaker_create_model sagemaker_create_model_bias_job_definition sagemaker_create_model_card sagemaker_create_model_card_export_job sagemaker_create_model_explainability_job_definition sagemaker_create_model_package sagemaker_create_model_package_group sagemaker_create_model_quality_job_definition sagemaker_create_monitoring_schedule sagemaker_create_notebook_instance sagemaker_create_notebook_instance_lifecycle_config sagemaker_create_pipeline sagemaker_create_presigned_domain_url sagemaker_create_presigned_notebook_instance_url sagemaker_create_processing_job sagemaker_create_project sagemaker_create_space sagemaker_create_studio_lifecycle_config sagemaker_create_training_job sagemaker_create_transform_job sagemaker_create_trial sagemaker_create_trial_component sagemaker_create_user_profile sagemaker_create_workforce sagemaker_create_workteam sagemaker_delete_action sagemaker_delete_algorithm sagemaker_delete_app sagemaker_delete_app_image_config sagemaker_delete_artifact sagemaker_delete_association sagemaker_delete_code_repository sagemaker_delete_context sagemaker_delete_data_quality_job_definition sagemaker_delete_device_fleet sagemaker_delete_domain sagemaker_delete_edge_deployment_plan sagemaker_delete_edge_deployment_stage sagemaker_delete_endpoint sagemaker_delete_endpoint_config sagemaker_delete_experiment sagemaker_delete_feature_group sagemaker_delete_flow_definition sagemaker_delete_hub sagemaker_delete_hub_content sagemaker_delete_human_task_ui sagemaker_delete_image sagemaker_delete_image_version sagemaker_delete_inference_experiment sagemaker_delete_model sagemaker_delete_model_bias_job_definition sagemaker_delete_model_card sagemaker_delete_model_explainability_job_definition sagemaker_delete_model_package sagemaker_delete_model_package_group sagemaker_delete_model_package_group_policy sagemaker_delete_model_quality_job_definition sagemaker_delete_monitoring_schedule sagemaker_delete_notebook_instance sagemaker_delete_notebook_instance_lifecycle_config sagemaker_delete_pipeline sagemaker_delete_project sagemaker_delete_space sagemaker_delete_studio_lifecycle_config sagemaker_delete_tags sagemaker_delete_trial sagemaker_delete_trial_component sagemaker_delete_user_profile sagemaker_delete_workforce sagemaker_delete_workteam sagemaker_deregister_devices sagemaker_describe_action sagemaker_describe_algorithm sagemaker_describe_app sagemaker_describe_app_image_config sagemaker_describe_artifact sagemaker_describe_auto_ml_job sagemaker_describe_auto_ml_job_v2 sagemaker_describe_code_repository sagemaker_describe_compilation_job sagemaker_describe_context sagemaker_describe_data_quality_job_definition sagemaker_describe_device sagemaker_describe_device_fleet sagemaker_describe_domain sagemaker_describe_edge_deployment_plan sagemaker_describe_edge_packaging_job sagemaker_describe_endpoint sagemaker_describe_endpoint_config sagemaker_describe_experiment sagemaker_describe_feature_group sagemaker_describe_feature_metadata sagemaker_describe_flow_definition sagemaker_describe_hub sagemaker_describe_hub_content sagemaker_describe_human_task_ui sagemaker_describe_hyper_parameter_tuning_job sagemaker_describe_image sagemaker_describe_image_version sagemaker_describe_inference_experiment sagemaker_describe_inference_recommendations_job sagemaker_describe_labeling_job sagemaker_describe_lineage_group sagemaker_describe_model sagemaker_describe_model_bias_job_definition sagemaker_describe_model_card sagemaker_describe_model_card_export_job sagemaker_describe_model_explainability_job_definition sagemaker_describe_model_package sagemaker_describe_model_package_group sagemaker_describe_model_quality_job_definition sagemaker_describe_monitoring_schedule sagemaker_describe_notebook_instance sagemaker_describe_notebook_instance_lifecycle_config sagemaker_describe_pipeline sagemaker_describe_pipeline_definition_for_execution sagemaker_describe_pipeline_execution sagemaker_describe_processing_job sagemaker_describe_project sagemaker_describe_space sagemaker_describe_studio_lifecycle_config sagemaker_describe_subscribed_workteam sagemaker_describe_training_job sagemaker_describe_transform_job sagemaker_describe_trial sagemaker_describe_trial_component sagemaker_describe_user_profile sagemaker_describe_workforce sagemaker_describe_workteam sagemaker_disable_sagemaker_servicecatalog_portfolio sagemaker_disassociate_trial_component sagemaker_enable_sagemaker_servicecatalog_portfolio sagemaker_get_device_fleet_report sagemaker_get_lineage_group_policy sagemaker_get_model_package_group_policy sagemaker_get_sagemaker_servicecatalog_portfolio_status sagemaker_get_scaling_configuration_recommendation sagemaker_get_search_suggestions sagemaker_import_hub_content sagemaker_list_actions sagemaker_list_algorithms sagemaker_list_aliases sagemaker_list_app_image_configs sagemaker_list_apps sagemaker_list_artifacts sagemaker_list_associations sagemaker_list_auto_ml_jobs sagemaker_list_candidates_for_auto_ml_job sagemaker_list_code_repositories sagemaker_list_compilation_jobs sagemaker_list_contexts sagemaker_list_data_quality_job_definitions sagemaker_list_device_fleets sagemaker_list_devices sagemaker_list_domains sagemaker_list_edge_deployment_plans sagemaker_list_edge_packaging_jobs sagemaker_list_endpoint_configs sagemaker_list_endpoints sagemaker_list_experiments sagemaker_list_feature_groups sagemaker_list_flow_definitions sagemaker_list_hub_contents sagemaker_list_hub_content_versions sagemaker_list_hubs sagemaker_list_human_task_uis sagemaker_list_hyper_parameter_tuning_jobs sagemaker_list_images sagemaker_list_image_versions sagemaker_list_inference_experiments sagemaker_list_inference_recommendations_jobs sagemaker_list_inference_recommendations_job_steps sagemaker_list_labeling_jobs sagemaker_list_labeling_jobs_for_workteam sagemaker_list_lineage_groups sagemaker_list_model_bias_job_definitions sagemaker_list_model_card_export_jobs sagemaker_list_model_cards sagemaker_list_model_card_versions sagemaker_list_model_explainability_job_definitions sagemaker_list_model_metadata sagemaker_list_model_package_groups sagemaker_list_model_packages sagemaker_list_model_quality_job_definitions sagemaker_list_models sagemaker_list_monitoring_alert_history sagemaker_list_monitoring_alerts sagemaker_list_monitoring_executions sagemaker_list_monitoring_schedules sagemaker_list_notebook_instance_lifecycle_configs sagemaker_list_notebook_instances sagemaker_list_pipeline_executions sagemaker_list_pipeline_execution_steps sagemaker_list_pipeline_parameters_for_execution sagemaker_list_pipelines sagemaker_list_processing_jobs sagemaker_list_projects sagemaker_list_resource_catalogs sagemaker_list_spaces sagemaker_list_stage_devices sagemaker_list_studio_lifecycle_configs sagemaker_list_subscribed_workteams sagemaker_list_tags sagemaker_list_training_jobs sagemaker_list_training_jobs_for_hyper_parameter_tuning_job sagemaker_list_transform_jobs sagemaker_list_trial_components sagemaker_list_trials sagemaker_list_user_profiles sagemaker_list_workforces sagemaker_list_workteams sagemaker_put_model_package_group_policy sagemaker_query_lineage sagemaker_register_devices sagemaker_render_ui_template sagemaker_retry_pipeline_execution sagemaker_search sagemaker_send_pipeline_execution_step_failure sagemaker_send_pipeline_execution_step_success sagemaker_start_edge_deployment_stage sagemaker_start_inference_experiment sagemaker_start_monitoring_schedule sagemaker_start_notebook_instance sagemaker_start_pipeline_execution sagemaker_stop_auto_ml_job sagemaker_stop_compilation_job sagemaker_stop_edge_deployment_stage sagemaker_stop_edge_packaging_job sagemaker_stop_hyper_parameter_tuning_job sagemaker_stop_inference_experiment sagemaker_stop_inference_recommendations_job sagemaker_stop_labeling_job sagemaker_stop_monitoring_schedule sagemaker_stop_notebook_instance sagemaker_stop_pipeline_execution sagemaker_stop_processing_job sagemaker_stop_training_job sagemaker_stop_transform_job sagemaker_update_action sagemaker_update_app_image_config sagemaker_update_artifact sagemaker_update_code_repository sagemaker_update_context sagemaker_update_device_fleet sagemaker_update_devices sagemaker_update_domain sagemaker_update_endpoint sagemaker_update_endpoint_weights_and_capacities sagemaker_update_experiment sagemaker_update_feature_group sagemaker_update_feature_metadata sagemaker_update_hub sagemaker_update_image sagemaker_update_image_version sagemaker_update_inference_experiment sagemaker_update_model_card sagemaker_update_model_package sagemaker_update_monitoring_alert sagemaker_update_monitoring_schedule sagemaker_update_notebook_instance sagemaker_update_notebook_instance_lifecycle_config sagemaker_update_pipeline sagemaker_update_pipeline_execution sagemaker_update_project sagemaker_update_space sagemaker_update_training_job sagemaker_update_trial sagemaker_update_trial_component sagemaker_update_user_profile sagemaker_update_workforce sagemaker_update_workteam

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include sagemaker_service.R
NULL

#' Creates an association between the source and the destination
#'
#' @description
#' Creates an *association* between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see [Amazon SageMaker ML Lineage Tracking](https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_add_association/](https://www.paws-r-sdk.com/docs/sagemaker_add_association/) for full documentation.
#'
#' @param SourceArn [required] The ARN of the source.
#' @param DestinationArn [required] The Amazon Resource Name (ARN) of the destination.
#' @param AssociationType The type of association. The following are suggested uses for each type.
#' Amazon SageMaker places no restrictions on their use.
#' 
#' -   ContributedTo - The source contributed to the destination or had a
#'     part in enabling the destination. For example, the training data
#'     contributed to the training job.
#' 
#' -   AssociatedWith - The source is connected to the destination. For
#'     example, an approval workflow is associated with a model deployment.
#' 
#' -   DerivedFrom - The destination is a modification of the source. For
#'     example, a digest output of a channel input for a processing job is
#'     derived from the original inputs.
#' 
#' -   Produced - The source generated the destination. For example, a
#'     training job produced a model artifact.
#'
#' @keywords internal
#'
#' @rdname sagemaker_add_association
sagemaker_add_association <- function(SourceArn, DestinationArn, AssociationType = NULL) {
  op <- new_operation(
    name = "AddAssociation",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$add_association_input(SourceArn = SourceArn, DestinationArn = DestinationArn, AssociationType = AssociationType)
  output <- .sagemaker$add_association_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$add_association <- sagemaker_add_association

#' Adds or overwrites one or more tags for the specified SageMaker resource
#'
#' @description
#' Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_add_tags/](https://www.paws-r-sdk.com/docs/sagemaker_add_tags/) for full documentation.
#'
#' @param ResourceArn &#91;required&#93; The Amazon Resource Name (ARN) of the resource that you want to tag.
#' @param Tags &#91;required&#93; An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_add_tags
sagemaker_add_tags <- function(ResourceArn, Tags) {
  op <- new_operation(
    name = "AddTags",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$add_tags_input(ResourceArn = ResourceArn, Tags = Tags)
  output <- .sagemaker$add_tags_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$add_tags <- sagemaker_add_tags

#' Associates a trial component with a trial
#'
#' @description
#' Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the [`disassociate_trial_component`][sagemaker_disassociate_trial_component] API.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_associate_trial_component/](https://www.paws-r-sdk.com/docs/sagemaker_associate_trial_component/) for full documentation.
#'
#' @param TrialComponentName &#91;required&#93; The name of the component to associated with the trial.
#' @param TrialName &#91;required&#93; The name of the trial to associate with.
#'
#' @keywords internal
#'
#' @rdname sagemaker_associate_trial_component
sagemaker_associate_trial_component <- function(TrialComponentName, TrialName) {
  op <- new_operation(
    name = "AssociateTrialComponent",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$associate_trial_component_input(TrialComponentName = TrialComponentName, TrialName = TrialName)
  output <- .sagemaker$associate_trial_component_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$associate_trial_component <- sagemaker_associate_trial_component

#' This action batch describes a list of versioned model packages
#'
#' @description
#' This action batch describes a list of versioned model packages
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_batch_describe_model_package/](https://www.paws-r-sdk.com/docs/sagemaker_batch_describe_model_package/) for full documentation.
#'
#' @param ModelPackageArnList &#91;required&#93; The list of Amazon Resource Name (ARN) of the model package groups.
#'
#' @keywords internal
#'
#' @rdname sagemaker_batch_describe_model_package
sagemaker_batch_describe_model_package <- function(ModelPackageArnList) {
  op <- new_operation(
    name = "BatchDescribeModelPackage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$batch_describe_model_package_input(ModelPackageArnList = ModelPackageArnList)
  output <- .sagemaker$batch_describe_model_package_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$batch_describe_model_package <- sagemaker_batch_describe_model_package

#' Creates an action
#'
#' @description
#' Creates an *action*. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see [Amazon SageMaker ML Lineage Tracking](https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_action/](https://www.paws-r-sdk.com/docs/sagemaker_create_action/) for full documentation.
#'
#' @param ActionName &#91;required&#93; The name of the action. Must be unique to your account in an Amazon Web
#' Services Region.
#' @param Source &#91;required&#93; The source type, ID, and URI.
#' @param ActionType &#91;required&#93; The action type.
#' @param Description The description of the action.
#' @param Status The status of the action.
#' @param Properties A list of properties to add to the action.
#' @param MetadataProperties 
#' @param Tags A list of tags to apply to the action.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_action
sagemaker_create_action <- function(ActionName, Source, ActionType, Description = NULL, Status = NULL, Properties = NULL, MetadataProperties = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateAction",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_action_input(ActionName = ActionName, Source = Source, ActionType = ActionType, Description = Description, Status = Status, Properties = Properties, MetadataProperties = MetadataProperties, Tags = Tags)
  output <- .sagemaker$create_action_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_action <- sagemaker_create_action

#' Create a machine learning algorithm that you can use in SageMaker and
#' list in the Amazon Web Services Marketplace
#'
#' @description
#' Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_algorithm/](https://www.paws-r-sdk.com/docs/sagemaker_create_algorithm/) for full documentation.
#'
#' @param AlgorithmName &#91;required&#93; The name of the algorithm.
#' @param AlgorithmDescription A description of the algorithm.
#' @param TrainingSpecification &#91;required&#93; Specifies details about training jobs run by this algorithm, including
#' the following:
#' 
#' -   The Amazon ECR path of the container and the version digest of the
#'     algorithm.
#' 
#' -   The hyperparameters that the algorithm supports.
#' 
#' -   The instance types that the algorithm supports for training.
#' 
#' -   Whether the algorithm supports distributed training.
#' 
#' -   The metrics that the algorithm emits to Amazon CloudWatch.
#' 
#' -   Which metrics that the algorithm emits can be used as the objective
#'     metric for hyperparameter tuning jobs.
#' 
#' -   The input channels that the algorithm supports for training data.
#'     For example, an algorithm might support `train`, `validation`, and
#'     `test` channels.
#' @param InferenceSpecification Specifies details about inference jobs that the algorithm runs,
#' including the following:
#' 
#' -   The Amazon ECR paths of containers that contain the inference code
#'     and model artifacts.
#' 
#' -   The instance types that the algorithm supports for transform jobs
#'     and real-time endpoints used for inference.
#' 
#' -   The input and output content formats that the algorithm supports for
#'     inference.
#' @param ValidationSpecification Specifies configurations for one or more training jobs and that
#' SageMaker runs to test the algorithm's training code and, optionally,
#' one or more batch transform jobs that SageMaker runs to test the
#' algorithm's inference code.
#' @param CertifyForMarketplace Whether to certify the algorithm so that it can be listed in Amazon Web
#' Services Marketplace.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_algorithm
sagemaker_create_algorithm <- function(AlgorithmName, AlgorithmDescription = NULL, TrainingSpecification, InferenceSpecification = NULL, ValidationSpecification = NULL, CertifyForMarketplace = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateAlgorithm",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_algorithm_input(AlgorithmName = AlgorithmName, AlgorithmDescription = AlgorithmDescription, TrainingSpecification = TrainingSpecification, InferenceSpecification = InferenceSpecification, ValidationSpecification = ValidationSpecification, CertifyForMarketplace = CertifyForMarketplace, Tags = Tags)
  output <- .sagemaker$create_algorithm_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_algorithm <- sagemaker_create_algorithm

#' Creates a running app for the specified UserProfile
#'
#' @description
#' Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_app/](https://www.paws-r-sdk.com/docs/sagemaker_create_app/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param UserProfileName The user profile name. If this value is not set, then `SpaceName` must
#' be set.
#' @param AppType &#91;required&#93; The type of app.
#' @param AppName &#91;required&#93; The name of the app.
#' @param Tags Each tag consists of a key and an optional value. Tag keys must be
#' unique per resource.
#' @param ResourceSpec The instance type and the Amazon Resource Name (ARN) of the SageMaker
#' image created on the instance.
#' 
#' The value of `InstanceType` passed as part of the `ResourceSpec` in the
#' [`create_app`][sagemaker_create_app] call overrides the value passed as
#' part of the `ResourceSpec` configured for the user profile or the
#' domain. If `InstanceType` is not specified in any of those three
#' `ResourceSpec` values for a `KernelGateway` app, the
#' [`create_app`][sagemaker_create_app] call fails with a request
#' validation error.
#' @param SpaceName The name of the space. If this value is not set, then `UserProfileName`
#' must be set.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_app
sagemaker_create_app <- function(DomainId, UserProfileName = NULL, AppType, AppName, Tags = NULL, ResourceSpec = NULL, SpaceName = NULL) {
  op <- new_operation(
    name = "CreateApp",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_app_input(DomainId = DomainId, UserProfileName = UserProfileName, AppType = AppType, AppName = AppName, Tags = Tags, ResourceSpec = ResourceSpec, SpaceName = SpaceName)
  output <- .sagemaker$create_app_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_app <- sagemaker_create_app

#' Creates a configuration for running a SageMaker image as a KernelGateway
#' app
#'
#' @description
#' Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_app_image_config/](https://www.paws-r-sdk.com/docs/sagemaker_create_app_image_config/) for full documentation.
#'
#' @param AppImageConfigName &#91;required&#93; The name of the AppImageConfig. Must be unique to your account.
#' @param Tags A list of tags to apply to the AppImageConfig.
#' @param KernelGatewayImageConfig The KernelGatewayImageConfig. You can only specify one image kernel in
#' the AppImageConfig API. This kernel will be shown to users before the
#' image starts. Once the image runs, all kernels are visible in
#' JupyterLab.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_app_image_config
sagemaker_create_app_image_config <- function(AppImageConfigName, Tags = NULL, KernelGatewayImageConfig = NULL) {
  op <- new_operation(
    name = "CreateAppImageConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_app_image_config_input(AppImageConfigName = AppImageConfigName, Tags = Tags, KernelGatewayImageConfig = KernelGatewayImageConfig)
  output <- .sagemaker$create_app_image_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_app_image_config <- sagemaker_create_app_image_config

#' Creates an artifact
#'
#' @description
#' Creates an *artifact*. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see [Amazon SageMaker ML Lineage Tracking](https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_artifact/](https://www.paws-r-sdk.com/docs/sagemaker_create_artifact/) for full documentation.
#'
#' @param ArtifactName The name of the artifact. Must be unique to your account in an Amazon
#' Web Services Region.
#' @param Source &#91;required&#93; The ID, ID type, and URI of the source.
#' @param ArtifactType &#91;required&#93; The artifact type.
#' @param Properties A list of properties to add to the artifact.
#' @param MetadataProperties 
#' @param Tags A list of tags to apply to the artifact.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_artifact
sagemaker_create_artifact <- function(ArtifactName = NULL, Source, ArtifactType, Properties = NULL, MetadataProperties = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateArtifact",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_artifact_input(ArtifactName = ArtifactName, Source = Source, ArtifactType = ArtifactType, Properties = Properties, MetadataProperties = MetadataProperties, Tags = Tags)
  output <- .sagemaker$create_artifact_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_artifact <- sagemaker_create_artifact

#' Creates an Autopilot job also referred to as Autopilot experiment or
#' AutoML job
#'
#' @description
#' Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job/) for full documentation.
#'
#' @param AutoMLJobName &#91;required&#93; Identifies an Autopilot job. The name must be unique to your account and
#' is case insensitive.
#' @param InputDataConfig &#91;required&#93; An array of channel objects that describes the input data and its
#' location. Each channel is a named input source. Similar to
#' `InputDataConfig` supported by
#' [HyperParameterTrainingJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html).
#' Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for
#' the training dataset. There is not a minimum number of rows required for
#' the validation dataset.
#' @param OutputDataConfig &#91;required&#93; Provides information about encryption and the Amazon S3 output path
#' needed to store artifacts from an AutoML job. Format(s) supported: CSV.
#' @param ProblemType Defines the type of supervised learning problem available for the
#' candidates. For more information, see [Amazon SageMaker Autopilot
#' problem
#' types](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types).
#' @param AutoMLJobObjective Specifies a metric to minimize or maximize as the objective of a job. If
#' not specified, the default objective metric depends on the problem type.
#' See
#' [AutoMLJobObjective](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html)
#' for the default values.
#' @param AutoMLJobConfig A collection of settings used to configure an AutoML job.
#' @param RoleArn &#91;required&#93; The ARN of the role that is used to access the data.
#' @param GenerateCandidateDefinitionsOnly Generates possible candidates without training the models. A candidate
#' is a combination of data preprocessors, algorithms, and algorithm
#' parameter settings.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' ServicesResources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' Tag keys must be unique per resource.
#' @param ModelDeployConfig Specifies how to generate the endpoint name for an automatic one-click
#' Autopilot model deployment.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_auto_ml_job
sagemaker_create_auto_ml_job <- function(AutoMLJobName, InputDataConfig, OutputDataConfig, ProblemType = NULL, AutoMLJobObjective = NULL, AutoMLJobConfig = NULL, RoleArn, GenerateCandidateDefinitionsOnly = NULL, Tags = NULL, ModelDeployConfig = NULL) {
  op <- new_operation(
    name = "CreateAutoMLJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_auto_ml_job_input(AutoMLJobName = AutoMLJobName, InputDataConfig = InputDataConfig, OutputDataConfig = OutputDataConfig, ProblemType = ProblemType, AutoMLJobObjective = AutoMLJobObjective, AutoMLJobConfig = AutoMLJobConfig, RoleArn = RoleArn, GenerateCandidateDefinitionsOnly = GenerateCandidateDefinitionsOnly, Tags = Tags, ModelDeployConfig = ModelDeployConfig)
  output <- .sagemaker$create_auto_ml_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_auto_ml_job <- sagemaker_create_auto_ml_job

#' Creates an Autopilot job also referred to as Autopilot experiment or
#' AutoML job V2
#'
#' @description
#' Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job_v2/](https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job_v2/) for full documentation.
#'
#' @param AutoMLJobName &#91;required&#93; Identifies an Autopilot job. The name must be unique to your account and
#' is case insensitive.
#' @param AutoMLJobInputDataConfig &#91;required&#93; An array of channel objects describing the input data and their
#' location. Each channel is a named input source. Similar to the
#' [InputDataConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig)
#' attribute in the [`create_auto_ml_job`][sagemaker_create_auto_ml_job]
#' input parameters. The supported formats depend on the problem type:
#' 
#' -   For tabular problem types: `S3Prefix`, `ManifestFile`.
#' 
#' -   For image classification: `S3Prefix`, `ManifestFile`,
#'     `AugmentedManifestFile`.
#' 
#' -   For text classification: `S3Prefix`.
#' 
#' -   For time-series forecasting: `S3Prefix`.
#' @param OutputDataConfig &#91;required&#93; Provides information about encryption and the Amazon S3 output path
#' needed to store artifacts from an AutoML job.
#' @param AutoMLProblemTypeConfig &#91;required&#93; Defines the configuration settings of one of the supported problem
#' types.
#' @param RoleArn &#91;required&#93; The ARN of the role that is used to access the data.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, such as by purpose, owner, or
#' environment. For more information, see [Tagging Amazon Web
#' ServicesResources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' Tag keys must be unique per resource.
#' @param SecurityConfig The security configuration for traffic encryption or Amazon VPC
#' settings.
#' @param AutoMLJobObjective Specifies a metric to minimize or maximize as the objective of a job. If
#' not specified, the default objective metric depends on the problem type.
#' For the list of default values per problem type, see
#' [AutoMLJobObjective](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html).
#' 
#' For tabular problem types, you must either provide both the
#' `AutoMLJobObjective` and indicate the type of supervised learning
#' problem in `AutoMLProblemTypeConfig` (`TabularJobConfig.ProblemType`),
#' or none at all.
#' @param ModelDeployConfig Specifies how to generate the endpoint name for an automatic one-click
#' Autopilot model deployment.
#' @param DataSplitConfig This structure specifies how to split the data into train and validation
#' datasets.
#' 
#' The validation and training datasets must contain the same headers. For
#' jobs created by calling
#' [`create_auto_ml_job`][sagemaker_create_auto_ml_job], the validation
#' dataset must be less than 2 GB in size.
#' 
#' This attribute must not be set for the time-series forecasting problem
#' type, as Autopilot automatically splits the input dataset into training
#' and validation sets.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_auto_ml_job_v2
sagemaker_create_auto_ml_job_v2 <- function(AutoMLJobName, AutoMLJobInputDataConfig, OutputDataConfig, AutoMLProblemTypeConfig, RoleArn, Tags = NULL, SecurityConfig = NULL, AutoMLJobObjective = NULL, ModelDeployConfig = NULL, DataSplitConfig = NULL) {
  op <- new_operation(
    name = "CreateAutoMLJobV2",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_auto_ml_job_v2_input(AutoMLJobName = AutoMLJobName, AutoMLJobInputDataConfig = AutoMLJobInputDataConfig, OutputDataConfig = OutputDataConfig, AutoMLProblemTypeConfig = AutoMLProblemTypeConfig, RoleArn = RoleArn, Tags = Tags, SecurityConfig = SecurityConfig, AutoMLJobObjective = AutoMLJobObjective, ModelDeployConfig = ModelDeployConfig, DataSplitConfig = DataSplitConfig)
  output <- .sagemaker$create_auto_ml_job_v2_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_auto_ml_job_v2 <- sagemaker_create_auto_ml_job_v2

#' Creates a Git repository as a resource in your SageMaker account
#'
#' @description
#' Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_code_repository/](https://www.paws-r-sdk.com/docs/sagemaker_create_code_repository/) for full documentation.
#'
#' @param CodeRepositoryName &#91;required&#93; The name of the Git repository. The name must have 1 to 63 characters.
#' Valid characters are a-z, A-Z, 0-9, and - (hyphen).
#' @param GitConfig &#91;required&#93; Specifies details about the repository, including the URL where the
#' repository is located, the default branch, and credentials to use to
#' access the repository.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_code_repository
sagemaker_create_code_repository <- function(CodeRepositoryName, GitConfig, Tags = NULL) {
  op <- new_operation(
    name = "CreateCodeRepository",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_code_repository_input(CodeRepositoryName = CodeRepositoryName, GitConfig = GitConfig, Tags = Tags)
  output <- .sagemaker$create_code_repository_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_code_repository <- sagemaker_create_code_repository

#' Starts a model compilation job
#'
#' @description
#' Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_compilation_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_compilation_job/) for full documentation.
#'
#' @param CompilationJobName &#91;required&#93; A name for the model compilation job. The name must be unique within the
#' Amazon Web Services Region and within your Amazon Web Services account.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that enables Amazon
#' SageMaker to perform tasks on your behalf.
#' 
#' During model compilation, Amazon SageMaker needs your permission to:
#' 
#' -   Read input data from an S3 bucket
#' 
#' -   Write model artifacts to an S3 bucket
#' 
#' -   Write logs to Amazon CloudWatch Logs
#' 
#' -   Publish metrics to Amazon CloudWatch
#' 
#' You grant permissions for all of these tasks to an IAM role. To pass
#' this role to Amazon SageMaker, the caller of this API must have the
#' `iam:PassRole` permission. For more information, see [Amazon SageMaker
#' Roles.](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)
#' @param ModelPackageVersionArn The Amazon Resource Name (ARN) of a versioned model package. Provide
#' either a `ModelPackageVersionArn` or an `InputConfig` object in the
#' request syntax. The presence of both objects in the
#' [`create_compilation_job`][sagemaker_create_compilation_job] request
#' will return an exception.
#' @param InputConfig Provides information about the location of input model artifacts, the
#' name and shape of the expected data inputs, and the framework in which
#' the model was trained.
#' @param OutputConfig &#91;required&#93; Provides information about the output location for the compiled model
#' and the target device the model runs on.
#' @param VpcConfig A
#' [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html)
#' object that specifies the VPC that you want your compilation job to
#' connect to. Control access to your models by configuring the VPC. For
#' more information, see [Protect Compilation Jobs by Using an Amazon
#' Virtual Private
#' Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html).
#' @param StoppingCondition &#91;required&#93; Specifies a limit to how long a model compilation job can run. When the
#' job reaches the time limit, Amazon SageMaker ends the compilation job.
#' Use this API to cap model training costs.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_compilation_job
sagemaker_create_compilation_job <- function(CompilationJobName, RoleArn, ModelPackageVersionArn = NULL, InputConfig = NULL, OutputConfig, VpcConfig = NULL, StoppingCondition, Tags = NULL) {
  op <- new_operation(
    name = "CreateCompilationJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_compilation_job_input(CompilationJobName = CompilationJobName, RoleArn = RoleArn, ModelPackageVersionArn = ModelPackageVersionArn, InputConfig = InputConfig, OutputConfig = OutputConfig, VpcConfig = VpcConfig, StoppingCondition = StoppingCondition, Tags = Tags)
  output <- .sagemaker$create_compilation_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_compilation_job <- sagemaker_create_compilation_job

#' Creates a context
#'
#' @description
#' Creates a *context*. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see [Amazon SageMaker ML Lineage Tracking](https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_context/](https://www.paws-r-sdk.com/docs/sagemaker_create_context/) for full documentation.
#'
#' @param ContextName &#91;required&#93; The name of the context. Must be unique to your account in an Amazon Web
#' Services Region.
#' @param Source &#91;required&#93; The source type, ID, and URI.
#' @param ContextType &#91;required&#93; The context type.
#' @param Description The description of the context.
#' @param Properties A list of properties to add to the context.
#' @param Tags A list of tags to apply to the context.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_context
sagemaker_create_context <- function(ContextName, Source, ContextType, Description = NULL, Properties = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateContext",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_context_input(ContextName = ContextName, Source = Source, ContextType = ContextType, Description = Description, Properties = Properties, Tags = Tags)
  output <- .sagemaker$create_context_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_context <- sagemaker_create_context

#' Creates a definition for a job that monitors data quality and drift
#'
#' @description
#' Creates a definition for a job that monitors data quality and drift. For information about model monitor, see [Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_data_quality_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_create_data_quality_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name for the monitoring job definition.
#' @param DataQualityBaselineConfig Configures the constraints and baselines for the monitoring job.
#' @param DataQualityAppSpecification &#91;required&#93; Specifies the container that runs the monitoring job.
#' @param DataQualityJobInput &#91;required&#93; A list of inputs for the monitoring job. Currently endpoints are
#' supported as monitoring inputs.
#' @param DataQualityJobOutputConfig &#91;required&#93; 
#' @param JobResources &#91;required&#93; 
#' @param NetworkConfig Specifies networking configuration for the monitoring job.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
#' assume to perform tasks on your behalf.
#' @param StoppingCondition 
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_data_quality_job_definition
sagemaker_create_data_quality_job_definition <- function(JobDefinitionName, DataQualityBaselineConfig = NULL, DataQualityAppSpecification, DataQualityJobInput, DataQualityJobOutputConfig, JobResources, NetworkConfig = NULL, RoleArn, StoppingCondition = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateDataQualityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_data_quality_job_definition_input(JobDefinitionName = JobDefinitionName, DataQualityBaselineConfig = DataQualityBaselineConfig, DataQualityAppSpecification = DataQualityAppSpecification, DataQualityJobInput = DataQualityJobInput, DataQualityJobOutputConfig = DataQualityJobOutputConfig, JobResources = JobResources, NetworkConfig = NetworkConfig, RoleArn = RoleArn, StoppingCondition = StoppingCondition, Tags = Tags)
  output <- .sagemaker$create_data_quality_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_data_quality_job_definition <- sagemaker_create_data_quality_job_definition

#' Creates a device fleet
#'
#' @description
#' Creates a device fleet.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_device_fleet/](https://www.paws-r-sdk.com/docs/sagemaker_create_device_fleet/) for full documentation.
#'
#' @param DeviceFleetName &#91;required&#93; The name of the fleet that the device belongs to.
#' @param RoleArn The Amazon Resource Name (ARN) that has access to Amazon Web Services
#' Internet of Things (IoT).
#' @param Description A description of the fleet.
#' @param OutputConfig &#91;required&#93; The output configuration for storing sample data collected by the fleet.
#' @param Tags Creates tags for the specified fleet.
#' @param EnableIotRoleAlias Whether to create an Amazon Web Services IoT Role Alias during device
#' fleet creation. The name of the role alias generated will match this
#' pattern: "SageMakerEdge-\{DeviceFleetName\}".
#' 
#' For example, if your device fleet is called "demo-fleet", the name of
#' the role alias will be "SageMakerEdge-demo-fleet".
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_device_fleet
sagemaker_create_device_fleet <- function(DeviceFleetName, RoleArn = NULL, Description = NULL, OutputConfig, Tags = NULL, EnableIotRoleAlias = NULL) {
  op <- new_operation(
    name = "CreateDeviceFleet",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_device_fleet_input(DeviceFleetName = DeviceFleetName, RoleArn = RoleArn, Description = Description, OutputConfig = OutputConfig, Tags = Tags, EnableIotRoleAlias = EnableIotRoleAlias)
  output <- .sagemaker$create_device_fleet_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_device_fleet <- sagemaker_create_device_fleet

#' Creates a Domain used by Amazon SageMaker Studio
#'
#' @description
#' Creates a `Domain` used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_domain/](https://www.paws-r-sdk.com/docs/sagemaker_create_domain/) for full documentation.
#'
#' @param DomainName &#91;required&#93; A name for the domain.
#' @param AuthMode &#91;required&#93; The mode of authentication that members use to access the domain.
#' @param DefaultUserSettings &#91;required&#93; The default settings to use to create a user profile when `UserSettings`
#' isn't specified in the call to the
#' [`create_user_profile`][sagemaker_create_user_profile] API.
#' 
#' `SecurityGroups` is aggregated when specified in both calls. For all
#' other settings in `UserSettings`, the values specified in
#' [`create_user_profile`][sagemaker_create_user_profile] take precedence
#' over those specified in [`create_domain`][sagemaker_create_domain].
#' @param SubnetIds &#91;required&#93; The VPC subnets that Studio uses for communication.
#' @param VpcId &#91;required&#93; The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for
#' communication.
#' @param Tags Tags to associated with the Domain. Each tag consists of a key and an
#' optional value. Tag keys must be unique per resource. Tags are
#' searchable using the [`search`][sagemaker_search] API.
#' 
#' Tags that you specify for the Domain are also added to all Apps that the
#' Domain launches.
#' @param AppNetworkAccessType Specifies the VPC used for non-EFS traffic. The default value is
#' `PublicInternetOnly`.
#' 
#' -   `PublicInternetOnly` - Non-EFS traffic is through a VPC managed by
#'     Amazon SageMaker, which allows direct internet access
#' 
#' -   `VpcOnly` - All Studio traffic is through the specified VPC and
#'     subnets
#' @param HomeEfsFileSystemKmsKeyId Use `KmsKeyId`.
#' @param KmsKeyId SageMaker uses Amazon Web Services KMS to encrypt the EFS volume
#' attached to the domain with an Amazon Web Services managed key by
#' default. For more control, specify a customer managed key.
#' @param AppSecurityGroupManagement The entity that creates and manages the required security groups for
#' inter-app communication in `VPCOnly` mode. Required when
#' `CreateDomain.AppNetworkAccessType` is `VPCOnly` and
#' `DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn`
#' is provided. If setting up the domain for use with RStudio, this value
#' must be set to `Service`.
#' @param DomainSettings A collection of `Domain` settings.
#' @param DefaultSpaceSettings The default settings used to create a space.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_domain
sagemaker_create_domain <- function(DomainName, AuthMode, DefaultUserSettings, SubnetIds, VpcId, Tags = NULL, AppNetworkAccessType = NULL, HomeEfsFileSystemKmsKeyId = NULL, KmsKeyId = NULL, AppSecurityGroupManagement = NULL, DomainSettings = NULL, DefaultSpaceSettings = NULL) {
  op <- new_operation(
    name = "CreateDomain",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_domain_input(DomainName = DomainName, AuthMode = AuthMode, DefaultUserSettings = DefaultUserSettings, SubnetIds = SubnetIds, VpcId = VpcId, Tags = Tags, AppNetworkAccessType = AppNetworkAccessType, HomeEfsFileSystemKmsKeyId = HomeEfsFileSystemKmsKeyId, KmsKeyId = KmsKeyId, AppSecurityGroupManagement = AppSecurityGroupManagement, DomainSettings = DomainSettings, DefaultSpaceSettings = DefaultSpaceSettings)
  output <- .sagemaker$create_domain_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_domain <- sagemaker_create_domain

#' Creates an edge deployment plan, consisting of multiple stages
#'
#' @description
#' Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_edge_deployment_plan/](https://www.paws-r-sdk.com/docs/sagemaker_create_edge_deployment_plan/) for full documentation.
#'
#' @param EdgeDeploymentPlanName &#91;required&#93; The name of the edge deployment plan.
#' @param ModelConfigs &#91;required&#93; List of models associated with the edge deployment plan.
#' @param DeviceFleetName &#91;required&#93; The device fleet used for this edge deployment plan.
#' @param Stages List of stages of the edge deployment plan. The number of stages is
#' limited to 10 per deployment.
#' @param Tags List of tags with which to tag the edge deployment plan.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_edge_deployment_plan
sagemaker_create_edge_deployment_plan <- function(EdgeDeploymentPlanName, ModelConfigs, DeviceFleetName, Stages = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateEdgeDeploymentPlan",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_edge_deployment_plan_input(EdgeDeploymentPlanName = EdgeDeploymentPlanName, ModelConfigs = ModelConfigs, DeviceFleetName = DeviceFleetName, Stages = Stages, Tags = Tags)
  output <- .sagemaker$create_edge_deployment_plan_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_edge_deployment_plan <- sagemaker_create_edge_deployment_plan

#' Creates a new stage in an existing edge deployment plan
#'
#' @description
#' Creates a new stage in an existing edge deployment plan.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_edge_deployment_stage/](https://www.paws-r-sdk.com/docs/sagemaker_create_edge_deployment_stage/) for full documentation.
#'
#' @param EdgeDeploymentPlanName &#91;required&#93; The name of the edge deployment plan.
#' @param Stages &#91;required&#93; List of stages to be added to the edge deployment plan.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_edge_deployment_stage
sagemaker_create_edge_deployment_stage <- function(EdgeDeploymentPlanName, Stages) {
  op <- new_operation(
    name = "CreateEdgeDeploymentStage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_edge_deployment_stage_input(EdgeDeploymentPlanName = EdgeDeploymentPlanName, Stages = Stages)
  output <- .sagemaker$create_edge_deployment_stage_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_edge_deployment_stage <- sagemaker_create_edge_deployment_stage

#' Starts a SageMaker Edge Manager model packaging job
#'
#' @description
#' Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_edge_packaging_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_edge_packaging_job/) for full documentation.
#'
#' @param EdgePackagingJobName &#91;required&#93; The name of the edge packaging job.
#' @param CompilationJobName &#91;required&#93; The name of the SageMaker Neo compilation job that will be used to
#' locate model artifacts for packaging.
#' @param ModelName &#91;required&#93; The name of the model.
#' @param ModelVersion &#91;required&#93; The version of the model.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that enables Amazon
#' SageMaker to download and upload the model, and to contact SageMaker
#' Neo.
#' @param OutputConfig &#91;required&#93; Provides information about the output location for the packaged model.
#' @param ResourceKey The Amazon Web Services KMS key to use when encrypting the EBS volume
#' the edge packaging job runs on.
#' @param Tags Creates tags for the packaging job.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_edge_packaging_job
sagemaker_create_edge_packaging_job <- function(EdgePackagingJobName, CompilationJobName, ModelName, ModelVersion, RoleArn, OutputConfig, ResourceKey = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateEdgePackagingJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_edge_packaging_job_input(EdgePackagingJobName = EdgePackagingJobName, CompilationJobName = CompilationJobName, ModelName = ModelName, ModelVersion = ModelVersion, RoleArn = RoleArn, OutputConfig = OutputConfig, ResourceKey = ResourceKey, Tags = Tags)
  output <- .sagemaker$create_edge_packaging_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_edge_packaging_job <- sagemaker_create_edge_packaging_job

#' Creates an endpoint using the endpoint configuration specified in the
#' request
#'
#' @description
#' Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the [`create_endpoint_config`][sagemaker_create_endpoint_config] API.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_endpoint/](https://www.paws-r-sdk.com/docs/sagemaker_create_endpoint/) for full documentation.
#'
#' @param EndpointName &#91;required&#93; The name of the endpoint.The name must be unique within an Amazon Web
#' Services Region in your Amazon Web Services account. The name is
#' case-insensitive in [`create_endpoint`][sagemaker_create_endpoint], but
#' the case is preserved and must be matched in
#' [InvokeEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html).
#' @param EndpointConfigName &#91;required&#93; The name of an endpoint configuration. For more information, see
#' [`create_endpoint_config`][sagemaker_create_endpoint_config].
#' @param DeploymentConfig 
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_endpoint
sagemaker_create_endpoint <- function(EndpointName, EndpointConfigName, DeploymentConfig = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateEndpoint",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_endpoint_input(EndpointName = EndpointName, EndpointConfigName = EndpointConfigName, DeploymentConfig = DeploymentConfig, Tags = Tags)
  output <- .sagemaker$create_endpoint_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_endpoint <- sagemaker_create_endpoint

#' Creates an endpoint configuration that SageMaker hosting services uses
#' to deploy models
#'
#' @description
#' Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the [`create_model`][sagemaker_create_model] API, to deploy and the resources that you want SageMaker to provision. Then you call the [`create_endpoint`][sagemaker_create_endpoint] API.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_endpoint_config/](https://www.paws-r-sdk.com/docs/sagemaker_create_endpoint_config/) for full documentation.
#'
#' @param EndpointConfigName &#91;required&#93; The name of the endpoint configuration. You specify this name in a
#' [`create_endpoint`][sagemaker_create_endpoint] request.
#' @param ProductionVariants &#91;required&#93; An array of `ProductionVariant` objects, one for each model that you
#' want to host at this endpoint.
#' @param DataCaptureConfig 
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' @param KmsKeyId The Amazon Resource Name (ARN) of a Amazon Web Services Key Management
#' Service key that SageMaker uses to encrypt data on the storage volume
#' attached to the ML compute instance that hosts the endpoint.
#' 
#' The KmsKeyId can be any of the following formats:
#' 
#' -   Key ID: `1234abcd-12ab-34cd-56ef-1234567890ab`
#' 
#' -   Key ARN:
#'     `arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab`
#' 
#' -   Alias name: `alias/ExampleAlias`
#' 
#' -   Alias name ARN:
#'     `arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias`
#' 
#' The KMS key policy must grant permission to the IAM role that you
#' specify in your [`create_endpoint`][sagemaker_create_endpoint],
#' [`update_endpoint`][sagemaker_update_endpoint] requests. For more
#' information, refer to the Amazon Web Services Key Management Service
#' section [Using Key Policies in Amazon Web Services
#' KMS](https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html)
#' 
#' Certain Nitro-based instances include local storage, dependent on the
#' instance type. Local storage volumes are encrypted using a hardware
#' module on the instance. You can't request a `KmsKeyId` when using an
#' instance type with local storage. If any of the models that you specify
#' in the `ProductionVariants` parameter use nitro-based instances with
#' local storage, do not specify a value for the `KmsKeyId` parameter. If
#' you specify a value for `KmsKeyId` when using any nitro-based instances
#' with local storage, the call to
#' [`create_endpoint_config`][sagemaker_create_endpoint_config] fails.
#' 
#' For a list of instance types that support local instance storage, see
#' [Instance Store
#' Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes).
#' 
#' For more information about local instance storage encryption, see [SSD
#' Instance Store
#' Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html).
#' @param AsyncInferenceConfig Specifies configuration for how an endpoint performs asynchronous
#' inference. This is a required field in order for your Endpoint to be
#' invoked using
#' [InvokeEndpointAsync](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpointAsync.html).
#' @param ExplainerConfig A member of [`create_endpoint_config`][sagemaker_create_endpoint_config]
#' that enables explainers.
#' @param ShadowProductionVariants An array of `ProductionVariant` objects, one for each model that you
#' want to host at this endpoint in shadow mode with production traffic
#' replicated from the model specified on `ProductionVariants`. If you use
#' this field, you can only specify one variant for `ProductionVariants`
#' and one variant for `ShadowProductionVariants`.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_endpoint_config
sagemaker_create_endpoint_config <- function(EndpointConfigName, ProductionVariants, DataCaptureConfig = NULL, Tags = NULL, KmsKeyId = NULL, AsyncInferenceConfig = NULL, ExplainerConfig = NULL, ShadowProductionVariants = NULL) {
  op <- new_operation(
    name = "CreateEndpointConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_endpoint_config_input(EndpointConfigName = EndpointConfigName, ProductionVariants = ProductionVariants, DataCaptureConfig = DataCaptureConfig, Tags = Tags, KmsKeyId = KmsKeyId, AsyncInferenceConfig = AsyncInferenceConfig, ExplainerConfig = ExplainerConfig, ShadowProductionVariants = ShadowProductionVariants)
  output <- .sagemaker$create_endpoint_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_endpoint_config <- sagemaker_create_endpoint_config

#' Creates a SageMaker experiment
#'
#' @description
#' Creates a SageMaker *experiment*. An experiment is a collection of *trials* that are observed, compared and evaluated as a group. A trial is a set of steps, called *trial components*, that produce a machine learning model.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_experiment/](https://www.paws-r-sdk.com/docs/sagemaker_create_experiment/) for full documentation.
#'
#' @param ExperimentName &#91;required&#93; The name of the experiment. The name must be unique in your Amazon Web
#' Services account and is not case-sensitive.
#' @param DisplayName The name of the experiment as displayed. The name doesn't need to be
#' unique. If you don't specify `DisplayName`, the value in
#' `ExperimentName` is displayed.
#' @param Description The description of the experiment.
#' @param Tags A list of tags to associate with the experiment. You can use
#' [`search`][sagemaker_search] API to search on the tags.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_experiment
sagemaker_create_experiment <- function(ExperimentName, DisplayName = NULL, Description = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateExperiment",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_experiment_input(ExperimentName = ExperimentName, DisplayName = DisplayName, Description = Description, Tags = Tags)
  output <- .sagemaker$create_experiment_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_experiment <- sagemaker_create_experiment

#' Create a new FeatureGroup
#'
#' @description
#' Create a new `FeatureGroup`. A `FeatureGroup` is a group of `Features` defined in the `FeatureStore` to describe a `Record`.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_feature_group/](https://www.paws-r-sdk.com/docs/sagemaker_create_feature_group/) for full documentation.
#'
#' @param FeatureGroupName &#91;required&#93; The name of the `FeatureGroup`. The name must be unique within an Amazon
#' Web Services Region in an Amazon Web Services account. The name:
#' 
#' -   Must start and end with an alphanumeric character.
#' 
#' -   Can only contain alphanumeric character and hyphens. Spaces are not
#'     allowed.
#' @param RecordIdentifierFeatureName &#91;required&#93; The name of the `Feature` whose value uniquely identifies a `Record`
#' defined in the `FeatureStore`. Only the latest record per identifier
#' value will be stored in the `OnlineStore`. `RecordIdentifierFeatureName`
#' must be one of feature definitions' names.
#' 
#' You use the `RecordIdentifierFeatureName` to access data in a
#' `FeatureStore`.
#' 
#' This name:
#' 
#' -   Must start and end with an alphanumeric character.
#' 
#' -   Can only contains alphanumeric characters, hyphens, underscores.
#'     Spaces are not allowed.
#' @param EventTimeFeatureName &#91;required&#93; The name of the feature that stores the `EventTime` of a `Record` in a
#' `FeatureGroup`.
#' 
#' An `EventTime` is a point in time when a new event occurs that
#' corresponds to the creation or update of a `Record` in a `FeatureGroup`.
#' All `Records` in the `FeatureGroup` must have a corresponding
#' `EventTime`.
#' 
#' An `EventTime` can be a `String` or `Fractional`.
#' 
#' -   `Fractional`: `EventTime` feature values must be a Unix timestamp in
#'     seconds.
#' 
#' -   `String`: `EventTime` feature values must be an ISO-8601 string in
#'     the format. The following formats are supported
#'     `yyyy-MM-dd'T'HH:mm:ssZ` and `yyyy-MM-dd'T'HH:mm:ss.SSSZ` where
#'     `yyyy`, `MM`, and `dd` represent the year, month, and day
#'     respectively and `HH`, `mm`, `ss`, and if applicable, `SSS`
#'     represent the hour, month, second and milliseconds respsectively.
#'     `'T'` and `Z` are constants.
#' @param FeatureDefinitions &#91;required&#93; A list of `Feature` names and types. `Name` and `Type` is compulsory per
#' `Feature`.
#' 
#' Valid feature `FeatureType`s are `Integral`, `Fractional` and `String`.
#' 
#' `FeatureName`s cannot be any of the following: `is_deleted`,
#' `write_time`, `api_invocation_time`
#' 
#' You can create up to 2,500 `FeatureDefinition`s per `FeatureGroup`.
#' @param OnlineStoreConfig You can turn the `OnlineStore` on or off by specifying `True` for the
#' `EnableOnlineStore` flag in `OnlineStoreConfig`.
#' 
#' You can also include an Amazon Web Services KMS key ID (`KMSKeyId`) for
#' at-rest encryption of the `OnlineStore`.
#' 
#' The default value is `False`.
#' @param OfflineStoreConfig Use this to configure an `OfflineFeatureStore`. This parameter allows
#' you to specify:
#' 
#' -   The Amazon Simple Storage Service (Amazon S3) location of an
#'     `OfflineStore`.
#' 
#' -   A configuration for an Amazon Web Services Glue or Amazon Web
#'     Services Hive data catalog.
#' 
#' -   An KMS encryption key to encrypt the Amazon S3 location used for
#'     `OfflineStore`. If KMS encryption key is not specified, by default
#'     we encrypt all data at rest using Amazon Web Services KMS key. By
#'     defining your [bucket-level
#'     key](https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucket-key.html)
#'     for SSE, you can reduce Amazon Web Services KMS requests costs by up
#'     to 99 percent.
#' 
#' -   Format for the offline store table. Supported formats are Glue
#'     (Default) and [Apache Iceberg](https://iceberg.apache.org/).
#' 
#' To learn more about this parameter, see
#' [OfflineStoreConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OfflineStoreConfig.html).
#' @param RoleArn The Amazon Resource Name (ARN) of the IAM execution role used to persist
#' data into the `OfflineStore` if an `OfflineStoreConfig` is provided.
#' @param Description A free-form description of a `FeatureGroup`.
#' @param Tags Tags used to identify `Features` in each `FeatureGroup`.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_feature_group
sagemaker_create_feature_group <- function(FeatureGroupName, RecordIdentifierFeatureName, EventTimeFeatureName, FeatureDefinitions, OnlineStoreConfig = NULL, OfflineStoreConfig = NULL, RoleArn = NULL, Description = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateFeatureGroup",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_feature_group_input(FeatureGroupName = FeatureGroupName, RecordIdentifierFeatureName = RecordIdentifierFeatureName, EventTimeFeatureName = EventTimeFeatureName, FeatureDefinitions = FeatureDefinitions, OnlineStoreConfig = OnlineStoreConfig, OfflineStoreConfig = OfflineStoreConfig, RoleArn = RoleArn, Description = Description, Tags = Tags)
  output <- .sagemaker$create_feature_group_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_feature_group <- sagemaker_create_feature_group

#' Creates a flow definition
#'
#' @description
#' Creates a flow definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_flow_definition/](https://www.paws-r-sdk.com/docs/sagemaker_create_flow_definition/) for full documentation.
#'
#' @param FlowDefinitionName &#91;required&#93; The name of your flow definition.
#' @param HumanLoopRequestSource Container for configuring the source of human task requests. Use to
#' specify if Amazon Rekognition or Amazon Textract is used as an
#' integration source.
#' @param HumanLoopActivationConfig An object containing information about the events that trigger a human
#' workflow.
#' @param HumanLoopConfig &#91;required&#93; An object containing information about the tasks the human reviewers
#' will perform.
#' @param OutputConfig &#91;required&#93; An object containing information about where the human review results
#' will be uploaded.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of the role needed to call other services
#' on your behalf. For example,
#' `arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298`.
#' @param Tags An array of key-value pairs that contain metadata to help you categorize
#' and organize a flow definition. Each tag consists of a key and a value,
#' both of which you define.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_flow_definition
sagemaker_create_flow_definition <- function(FlowDefinitionName, HumanLoopRequestSource = NULL, HumanLoopActivationConfig = NULL, HumanLoopConfig, OutputConfig, RoleArn, Tags = NULL) {
  op <- new_operation(
    name = "CreateFlowDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_flow_definition_input(FlowDefinitionName = FlowDefinitionName, HumanLoopRequestSource = HumanLoopRequestSource, HumanLoopActivationConfig = HumanLoopActivationConfig, HumanLoopConfig = HumanLoopConfig, OutputConfig = OutputConfig, RoleArn = RoleArn, Tags = Tags)
  output <- .sagemaker$create_flow_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_flow_definition <- sagemaker_create_flow_definition

#' Create a hub
#'
#' @description
#' Create a hub.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_hub/](https://www.paws-r-sdk.com/docs/sagemaker_create_hub/) for full documentation.
#'
#' @param HubName &#91;required&#93; The name of the hub to create.
#' @param HubDescription &#91;required&#93; A description of the hub.
#' @param HubDisplayName The display name of the hub.
#' @param HubSearchKeywords The searchable keywords for the hub.
#' @param S3StorageConfig The Amazon S3 storage configuration for the hub.
#' @param Tags Any tags to associate with the hub.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_hub
sagemaker_create_hub <- function(HubName, HubDescription, HubDisplayName = NULL, HubSearchKeywords = NULL, S3StorageConfig = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateHub",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_hub_input(HubName = HubName, HubDescription = HubDescription, HubDisplayName = HubDisplayName, HubSearchKeywords = HubSearchKeywords, S3StorageConfig = S3StorageConfig, Tags = Tags)
  output <- .sagemaker$create_hub_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_hub <- sagemaker_create_hub

#' Defines the settings you will use for the human review workflow user
#' interface
#'
#' @description
#' Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_human_task_ui/](https://www.paws-r-sdk.com/docs/sagemaker_create_human_task_ui/) for full documentation.
#'
#' @param HumanTaskUiName &#91;required&#93; The name of the user interface you are creating.
#' @param UiTemplate &#91;required&#93; 
#' @param Tags An array of key-value pairs that contain metadata to help you categorize
#' and organize a human review workflow user interface. Each tag consists
#' of a key and a value, both of which you define.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_human_task_ui
sagemaker_create_human_task_ui <- function(HumanTaskUiName, UiTemplate, Tags = NULL) {
  op <- new_operation(
    name = "CreateHumanTaskUi",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_human_task_ui_input(HumanTaskUiName = HumanTaskUiName, UiTemplate = UiTemplate, Tags = Tags)
  output <- .sagemaker$create_human_task_ui_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_human_task_ui <- sagemaker_create_human_task_ui

#' Starts a hyperparameter tuning job
#'
#' @description
#' Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_hyper_parameter_tuning_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_hyper_parameter_tuning_job/) for full documentation.
#'
#' @param HyperParameterTuningJobName &#91;required&#93; The name of the tuning job. This name is the prefix for the names of all
#' training jobs that this tuning job launches. The name must be unique
#' within the same Amazon Web Services account and Amazon Web Services
#' Region. The name must have 1 to 32 characters. Valid characters are a-z,
#' A-Z, 0-9, and : + = @@ _ % - (hyphen). The name is not case sensitive.
#' @param HyperParameterTuningJobConfig &#91;required&#93; The
#' [HyperParameterTuningJobConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html)
#' object that describes the tuning job, including the search strategy, the
#' objective metric used to evaluate training jobs, ranges of parameters to
#' search, and resource limits for the tuning job. For more information,
#' see [How Hyperparameter Tuning
#' Works](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html).
#' @param TrainingJobDefinition The
#' [HyperParameterTrainingJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html)
#' object that describes the training jobs that this tuning job launches,
#' including static hyperparameters, input data configuration, output data
#' configuration, resource configuration, and stopping condition.
#' @param TrainingJobDefinitions A list of the
#' [HyperParameterTrainingJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html)
#' objects launched for this tuning job.
#' @param WarmStartConfig Specifies the configuration for starting the hyperparameter tuning job
#' using one or more previous tuning jobs as a starting point. The results
#' of previous tuning jobs are used to inform which combinations of
#' hyperparameters to search over in the new tuning job.
#' 
#' All training jobs launched by the new hyperparameter tuning job are
#' evaluated by using the objective metric. If you specify
#' `IDENTICAL_DATA_AND_ALGORITHM` as the `WarmStartType` value for the warm
#' start configuration, the training job that performs the best in the new
#' tuning job is compared to the best training jobs from the parent tuning
#' jobs. From these, the training job that performs the best as measured by
#' the objective metric is returned as the overall best training job.
#' 
#' All training jobs launched by parent hyperparameter tuning jobs and the
#' new hyperparameter tuning jobs count against the limit of training jobs
#' for the tuning job.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' 
#' Tags that you specify for the tuning job are also added to all training
#' jobs that the tuning job launches.
#' @param Autotune Configures SageMaker Automatic model tuning (AMT) to automatically find
#' optimal parameters for the following fields:
#' 
#' -   [ParameterRanges](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-ParameterRanges):
#'     The names and ranges of parameters that a hyperparameter tuning job
#'     can optimize.
#' 
#' -   [ResourceLimits](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html):
#'     The maximum resources that can be used for a training job. These
#'     resources include the maximum number of training jobs, the maximum
#'     runtime of a tuning job, and the maximum number of training jobs to
#'     run at the same time.
#' 
#' -   [TrainingJobEarlyStoppingType](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-TrainingJobEarlyStoppingType):
#'     A flag that specifies whether or not to use early stopping for
#'     training jobs launched by a hyperparameter tuning job.
#' 
#' -   [RetryStrategy](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-RetryStrategy):
#'     The number of times to retry a training job.
#' 
#' -   [Strategy](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html):
#'     Specifies how hyperparameter tuning chooses the combinations of
#'     hyperparameter values to use for the training jobs that it launches.
#' 
#' -   [ConvergenceDetected](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ConvergenceDetected.html):
#'     A flag to indicate that Automatic model tuning (AMT) has detected
#'     model convergence.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_hyper_parameter_tuning_job
sagemaker_create_hyper_parameter_tuning_job <- function(HyperParameterTuningJobName, HyperParameterTuningJobConfig, TrainingJobDefinition = NULL, TrainingJobDefinitions = NULL, WarmStartConfig = NULL, Tags = NULL, Autotune = NULL) {
  op <- new_operation(
    name = "CreateHyperParameterTuningJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_hyper_parameter_tuning_job_input(HyperParameterTuningJobName = HyperParameterTuningJobName, HyperParameterTuningJobConfig = HyperParameterTuningJobConfig, TrainingJobDefinition = TrainingJobDefinition, TrainingJobDefinitions = TrainingJobDefinitions, WarmStartConfig = WarmStartConfig, Tags = Tags, Autotune = Autotune)
  output <- .sagemaker$create_hyper_parameter_tuning_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_hyper_parameter_tuning_job <- sagemaker_create_hyper_parameter_tuning_job

#' Creates a custom SageMaker image
#'
#' @description
#' Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see [Bring your own SageMaker image](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_image/](https://www.paws-r-sdk.com/docs/sagemaker_create_image/) for full documentation.
#'
#' @param Description The description of the image.
#' @param DisplayName The display name of the image. If not provided, `ImageName` is
#' displayed.
#' @param ImageName &#91;required&#93; The name of the image. Must be unique to your account.
#' @param RoleArn &#91;required&#93; The ARN of an IAM role that enables Amazon SageMaker to perform tasks on
#' your behalf.
#' @param Tags A list of tags to apply to the image.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_image
sagemaker_create_image <- function(Description = NULL, DisplayName = NULL, ImageName, RoleArn, Tags = NULL) {
  op <- new_operation(
    name = "CreateImage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_image_input(Description = Description, DisplayName = DisplayName, ImageName = ImageName, RoleArn = RoleArn, Tags = Tags)
  output <- .sagemaker$create_image_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_image <- sagemaker_create_image

#' Creates a version of the SageMaker image specified by ImageName
#'
#' @description
#' Creates a version of the SageMaker image specified by `ImageName`. The version represents the Amazon Elastic Container Registry (ECR) container image specified by `BaseImage`.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_image_version/](https://www.paws-r-sdk.com/docs/sagemaker_create_image_version/) for full documentation.
#'
#' @param BaseImage &#91;required&#93; The registry path of the container image to use as the starting point
#' for this version. The path is an Amazon Elastic Container Registry (ECR)
#' URI in the following format:
#' 
#' `<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@@digest]>`
#' @param ClientToken &#91;required&#93; A unique ID. If not specified, the Amazon Web Services CLI and Amazon
#' Web Services SDKs, such as the SDK for Python (Boto3), add a unique
#' value to the call.
#' @param ImageName &#91;required&#93; The `ImageName` of the `Image` to create a version of.
#' @param Aliases A list of aliases created with the image version.
#' @param VendorGuidance The stability of the image version, specified by the maintainer.
#' 
#' -   `NOT_PROVIDED`: The maintainers did not provide a status for image
#'     version stability.
#' 
#' -   `STABLE`: The image version is stable.
#' 
#' -   `TO_BE_ARCHIVED`: The image version is set to be archived. Custom
#'     image versions that are set to be archived are automatically
#'     archived after three months.
#' 
#' -   `ARCHIVED`: The image version is archived. Archived image versions
#'     are not searchable and are no longer actively supported.
#' @param JobType Indicates SageMaker job type compatibility.
#' 
#' -   `TRAINING`: The image version is compatible with SageMaker training
#'     jobs.
#' 
#' -   `INFERENCE`: The image version is compatible with SageMaker
#'     inference jobs.
#' 
#' -   `NOTEBOOK_KERNEL`: The image version is compatible with SageMaker
#'     notebook kernels.
#' @param MLFramework The machine learning framework vended in the image version.
#' @param ProgrammingLang The supported programming language and its version.
#' @param Processor Indicates CPU or GPU compatibility.
#' 
#' -   `CPU`: The image version is compatible with CPU.
#' 
#' -   `GPU`: The image version is compatible with GPU.
#' @param Horovod Indicates Horovod compatibility.
#' @param ReleaseNotes The maintainer description of the image version.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_image_version
sagemaker_create_image_version <- function(BaseImage, ClientToken, ImageName, Aliases = NULL, VendorGuidance = NULL, JobType = NULL, MLFramework = NULL, ProgrammingLang = NULL, Processor = NULL, Horovod = NULL, ReleaseNotes = NULL) {
  op <- new_operation(
    name = "CreateImageVersion",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_image_version_input(BaseImage = BaseImage, ClientToken = ClientToken, ImageName = ImageName, Aliases = Aliases, VendorGuidance = VendorGuidance, JobType = JobType, MLFramework = MLFramework, ProgrammingLang = ProgrammingLang, Processor = Processor, Horovod = Horovod, ReleaseNotes = ReleaseNotes)
  output <- .sagemaker$create_image_version_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_image_version <- sagemaker_create_image_version

#' Creates an inference experiment using the configurations specified in
#' the request
#'
#' @description
#' Creates an inference experiment using the configurations specified in the request.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_inference_experiment/](https://www.paws-r-sdk.com/docs/sagemaker_create_inference_experiment/) for full documentation.
#'
#' @param Name &#91;required&#93; The name for the inference experiment.
#' @param Type &#91;required&#93; The type of the inference experiment that you want to run. The following
#' types of experiments are possible:
#' 
#' -   `ShadowMode`: You can use this type to validate a shadow variant.
#'     For more information, see [Shadow
#'     tests](https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html).
#' @param Schedule The duration for which you want the inference experiment to run. If you
#' don't specify this field, the experiment automatically starts
#' immediately upon creation and concludes after 7 days.
#' @param Description A description for the inference experiment.
#' @param RoleArn &#91;required&#93; The ARN of the IAM role that Amazon SageMaker can assume to access model
#' artifacts and container images, and manage Amazon SageMaker Inference
#' endpoints for model deployment.
#' @param EndpointName &#91;required&#93; The name of the Amazon SageMaker endpoint on which you want to run the
#' inference experiment.
#' @param ModelVariants &#91;required&#93; An array of `ModelVariantConfig` objects. There is one for each variant
#' in the inference experiment. Each `ModelVariantConfig` object in the
#' array describes the infrastructure configuration for the corresponding
#' variant.
#' @param DataStorageConfig The Amazon S3 location and configuration for storing inference request
#' and response data.
#' 
#' This is an optional parameter that you can use for data capture. For
#' more information, see [Capture
#' data](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html).
#' @param ShadowModeConfig &#91;required&#93; The configuration of `ShadowMode` inference experiment type. Use this
#' field to specify a production variant which takes all the inference
#' requests, and a shadow variant to which Amazon SageMaker replicates a
#' percentage of the inference requests. For the shadow variant also
#' specify the percentage of requests that Amazon SageMaker replicates.
#' @param KmsKey The Amazon Web Services Key Management Service (Amazon Web Services KMS)
#' key that Amazon SageMaker uses to encrypt data on the storage volume
#' attached to the ML compute instance that hosts the endpoint. The
#' `KmsKey` can be any of the following formats:
#' 
#' -   KMS key ID
#' 
#'     `"1234abcd-12ab-34cd-56ef-1234567890ab"`
#' 
#' -   Amazon Resource Name (ARN) of a KMS key
#' 
#'     `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"`
#' 
#' -   KMS key Alias
#' 
#'     `"alias/ExampleAlias"`
#' 
#' -   Amazon Resource Name (ARN) of a KMS key Alias
#' 
#'     `"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"`
#' 
#' If you use a KMS key ID or an alias of your KMS key, the Amazon
#' SageMaker execution role must include permissions to call `kms:Encrypt`.
#' If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS
#' key for Amazon S3 for your role's account. Amazon SageMaker uses
#' server-side encryption with KMS managed keys for `OutputDataConfig`. If
#' you use a bucket policy with an `s3:PutObject` permission that only
#' allows objects with server-side encryption, set the condition key of
#' `s3:x-amz-server-side-encryption` to `"aws:kms"`. For more information,
#' see [KMS managed Encryption
#' Keys](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html)
#' in the *Amazon Simple Storage Service Developer Guide.*
#' 
#' The KMS key policy must grant permission to the IAM role that you
#' specify in your [`create_endpoint`][sagemaker_create_endpoint] and
#' [`update_endpoint`][sagemaker_update_endpoint] requests. For more
#' information, see [Using Key Policies in Amazon Web Services
#' KMS](https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html)
#' in the *Amazon Web Services Key Management Service Developer Guide*.
#' @param Tags Array of key-value pairs. You can use tags to categorize your Amazon Web
#' Services resources in different ways, for example, by purpose, owner, or
#' environment. For more information, see [Tagging your Amazon Web Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_inference_experiment
sagemaker_create_inference_experiment <- function(Name, Type, Schedule = NULL, Description = NULL, RoleArn, EndpointName, ModelVariants, DataStorageConfig = NULL, ShadowModeConfig, KmsKey = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateInferenceExperiment",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_inference_experiment_input(Name = Name, Type = Type, Schedule = Schedule, Description = Description, RoleArn = RoleArn, EndpointName = EndpointName, ModelVariants = ModelVariants, DataStorageConfig = DataStorageConfig, ShadowModeConfig = ShadowModeConfig, KmsKey = KmsKey, Tags = Tags)
  output <- .sagemaker$create_inference_experiment_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_inference_experiment <- sagemaker_create_inference_experiment

#' Starts a recommendation job
#'
#' @description
#' Starts a recommendation job. You can create either an instance recommendation or load test job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_inference_recommendations_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_inference_recommendations_job/) for full documentation.
#'
#' @param JobName &#91;required&#93; A name for the recommendation job. The name must be unique within the
#' Amazon Web Services Region and within your Amazon Web Services account.
#' The job name is passed down to the resources created by the
#' recommendation job. The names of resources (such as the model, endpoint
#' configuration, endpoint, and compilation) that are prefixed with the job
#' name are truncated at 40 characters.
#' @param JobType &#91;required&#93; Defines the type of recommendation job. Specify `Default` to initiate an
#' instance recommendation and `Advanced` to initiate a load test. If left
#' unspecified, Amazon SageMaker Inference Recommender will run an instance
#' recommendation (`DEFAULT`) job.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that enables Amazon
#' SageMaker to perform tasks on your behalf.
#' @param InputConfig &#91;required&#93; Provides information about the versioned model package Amazon Resource
#' Name (ARN), the traffic pattern, and endpoint configurations.
#' @param JobDescription Description of the recommendation job.
#' @param StoppingConditions A set of conditions for stopping a recommendation job. If any of the
#' conditions are met, the job is automatically stopped.
#' @param OutputConfig Provides information about the output artifacts and the KMS key to use
#' for Amazon S3 server-side encryption.
#' @param Tags The metadata that you apply to Amazon Web Services resources to help you
#' categorize and organize them. Each tag consists of a key and a value,
#' both of which you define. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html)
#' in the Amazon Web Services General Reference.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_inference_recommendations_job
sagemaker_create_inference_recommendations_job <- function(JobName, JobType, RoleArn, InputConfig, JobDescription = NULL, StoppingConditions = NULL, OutputConfig = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateInferenceRecommendationsJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_inference_recommendations_job_input(JobName = JobName, JobType = JobType, RoleArn = RoleArn, InputConfig = InputConfig, JobDescription = JobDescription, StoppingConditions = StoppingConditions, OutputConfig = OutputConfig, Tags = Tags)
  output <- .sagemaker$create_inference_recommendations_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_inference_recommendations_job <- sagemaker_create_inference_recommendations_job

#' Creates a job that uses workers to label the data objects in your input
#' dataset
#'
#' @description
#' Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_labeling_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_labeling_job/) for full documentation.
#'
#' @param LabelingJobName &#91;required&#93; The name of the labeling job. This name is used to identify the job in a
#' list of labeling jobs. Labeling job names must be unique within an
#' Amazon Web Services account and region. `LabelingJobName` is not case
#' sensitive. For example, Example-job and example-job are considered the
#' same labeling job name by Ground Truth.
#' @param LabelAttributeName &#91;required&#93; The attribute name to use for the label in the output manifest file.
#' This is the key for the key/value pair formed with the label that a
#' worker assigns to the object. The `LabelAttributeName` must meet the
#' following requirements.
#' 
#' -   The name can't end with "-metadata".
#' 
#' -   If you are using one of the following [built-in task
#'     types](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html),
#'     the attribute name *must* end with "-ref". If the task type you are
#'     using is not listed below, the attribute name *must not* end with
#'     "-ref".
#' 
#'     -   Image semantic segmentation (`SemanticSegmentation)`, and
#'         adjustment (`AdjustmentSemanticSegmentation`) and verification
#'         (`VerificationSemanticSegmentation`) labeling jobs for this task
#'         type.
#' 
#'     -   Video frame object detection (`VideoObjectDetection`), and
#'         adjustment and verification (`AdjustmentVideoObjectDetection`)
#'         labeling jobs for this task type.
#' 
#'     -   Video frame object tracking (`VideoObjectTracking`), and
#'         adjustment and verification (`AdjustmentVideoObjectTracking`)
#'         labeling jobs for this task type.
#' 
#'     -   3D point cloud semantic segmentation
#'         (`3DPointCloudSemanticSegmentation`), and adjustment and
#'         verification (`Adjustment3DPointCloudSemanticSegmentation`)
#'         labeling jobs for this task type.
#' 
#'     -   3D point cloud object tracking (`3DPointCloudObjectTracking`),
#'         and adjustment and verification
#'         (`Adjustment3DPointCloudObjectTracking`) labeling jobs for this
#'         task type.
#' 
#' If you are creating an adjustment or verification labeling job, you must
#' use a *different* `LabelAttributeName` than the one used in the original
#' labeling job. The original labeling job is the Ground Truth labeling job
#' that produced the labels that you want verified or adjusted. To learn
#' more about adjustment and verification labeling jobs, see [Verify and
#' Adjust
#' Labels](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html).
#' @param InputConfig &#91;required&#93; Input data for the labeling job, such as the Amazon S3 location of the
#' data objects and the location of the manifest file that describes the
#' data objects.
#' 
#' You must specify at least one of the following: `S3DataSource` or
#' `SnsDataSource`.
#' 
#' -   Use `SnsDataSource` to specify an SNS input topic for a streaming
#'     labeling job. If you do not specify and SNS input topic ARN, Ground
#'     Truth will create a one-time labeling job that stops after all data
#'     objects in the input manifest file have been labeled.
#' 
#' -   Use `S3DataSource` to specify an input manifest file for both
#'     streaming and one-time labeling jobs. Adding an `S3DataSource` is
#'     optional if you use `SnsDataSource` to create a streaming labeling
#'     job.
#' 
#' If you use the Amazon Mechanical Turk workforce, your input data should
#' not include confidential information, personal information or protected
#' health information. Use `ContentClassifiers` to specify that your data
#' is free of personally identifiable information and adult content.
#' @param OutputConfig &#91;required&#93; The location of the output data and the Amazon Web Services Key
#' Management Service key ID for the key used to encrypt the output data,
#' if any.
#' @param RoleArn &#91;required&#93; The Amazon Resource Number (ARN) that Amazon SageMaker assumes to
#' perform tasks on your behalf during data labeling. You must grant this
#' role the necessary permissions so that Amazon SageMaker can successfully
#' complete data labeling.
#' @param LabelCategoryConfigS3Uri The S3 URI of the file, referred to as a *label category configuration
#' file*, that defines the categories used to label the data objects.
#' 
#' For 3D point cloud and video frame task types, you can add label
#' category attributes and frame attributes to your label category
#' configuration file. To learn how, see [Create a Labeling Category
#' Configuration File for 3D Point Cloud Labeling
#' Jobs](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-label-cat-config-attributes.html).
#' 
#' For named entity recognition jobs, in addition to `"labels"`, you must
#' provide worker instructions in the label category configuration file
#' using the `"instructions"` parameter:
#' `"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}`.
#' For details and an example, see [Create a Named Entity Recognition
#' Labeling Job
#' (API)](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api)
#' .
#' 
#' For all other [built-in task
#' types](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html)
#' and [custom
#' tasks](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html),
#' your label category configuration file must be a JSON file in the
#' following format. Identify the labels you want to use by replacing
#' `label_1`, `label_2`,`...`,`label_n` with your label categories.
#' 
#' `\{ `
#' 
#' `"document-version": "2018-11-28",`
#' 
#' `"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]`
#' 
#' `\}`
#' 
#' Note the following about the label category configuration file:
#' 
#' -   For image classification and text classification (single and
#'     multi-label) you must specify at least two label categories. For all
#'     other task types, the minimum number of label categories required is
#'     one.
#' 
#' -   Each label category must be unique, you cannot specify duplicate
#'     label categories.
#' 
#' -   If you create a 3D point cloud or video frame adjustment or
#'     verification labeling job, you must include
#'     `auditLabelAttributeName` in the label category configuration. Use
#'     this parameter to enter the
#'     [`LabelAttributeName`](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName)
#'     of the labeling job you want to adjust or verify annotations of.
#' @param StoppingConditions A set of conditions for stopping the labeling job. If any of the
#' conditions are met, the job is automatically stopped. You can use these
#' conditions to control the cost of data labeling.
#' @param LabelingJobAlgorithmsConfig Configures the information required to perform automated data labeling.
#' @param HumanTaskConfig &#91;required&#93; Configures the labeling task and how it is presented to workers;
#' including, but not limited to price, keywords, and batch size (task
#' count).
#' @param Tags An array of key/value pairs. For more information, see [Using Cost
#' Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_labeling_job
sagemaker_create_labeling_job <- function(LabelingJobName, LabelAttributeName, InputConfig, OutputConfig, RoleArn, LabelCategoryConfigS3Uri = NULL, StoppingConditions = NULL, LabelingJobAlgorithmsConfig = NULL, HumanTaskConfig, Tags = NULL) {
  op <- new_operation(
    name = "CreateLabelingJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_labeling_job_input(LabelingJobName = LabelingJobName, LabelAttributeName = LabelAttributeName, InputConfig = InputConfig, OutputConfig = OutputConfig, RoleArn = RoleArn, LabelCategoryConfigS3Uri = LabelCategoryConfigS3Uri, StoppingConditions = StoppingConditions, LabelingJobAlgorithmsConfig = LabelingJobAlgorithmsConfig, HumanTaskConfig = HumanTaskConfig, Tags = Tags)
  output <- .sagemaker$create_labeling_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_labeling_job <- sagemaker_create_labeling_job

#' Creates a model in SageMaker
#'
#' @description
#' Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model/](https://www.paws-r-sdk.com/docs/sagemaker_create_model/) for full documentation.
#'
#' @param ModelName &#91;required&#93; The name of the new model.
#' @param PrimaryContainer The location of the primary docker image containing inference code,
#' associated artifacts, and custom environment map that the inference code
#' uses when the model is deployed for predictions.
#' @param Containers Specifies the containers in the inference pipeline.
#' @param InferenceExecutionConfig Specifies details of how containers in a multi-container endpoint are
#' called.
#' @param ExecutionRoleArn &#91;required&#93; The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume
#' to access model artifacts and docker image for deployment on ML compute
#' instances or for batch transform jobs. Deploying on ML compute instances
#' is part of model hosting. For more information, see [SageMaker
#' Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).
#' 
#' To be able to pass this role to SageMaker, the caller of this API must
#' have the `iam:PassRole` permission.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' @param VpcConfig A
#' [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html)
#' object that specifies the VPC that you want your model to connect to.
#' Control access to and from your model container by configuring the VPC.
#' `VpcConfig` is used in hosting services and in batch transform. For more
#' information, see [Protect Endpoints by Using an Amazon Virtual Private
#' Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html)
#' and [Protect Data in Batch Transform Jobs by Using an Amazon Virtual
#' Private
#' Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html).
#' @param EnableNetworkIsolation Isolates the model container. No inbound or outbound network calls can
#' be made to or from the model container.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model
sagemaker_create_model <- function(ModelName, PrimaryContainer = NULL, Containers = NULL, InferenceExecutionConfig = NULL, ExecutionRoleArn, Tags = NULL, VpcConfig = NULL, EnableNetworkIsolation = NULL) {
  op <- new_operation(
    name = "CreateModel",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_input(ModelName = ModelName, PrimaryContainer = PrimaryContainer, Containers = Containers, InferenceExecutionConfig = InferenceExecutionConfig, ExecutionRoleArn = ExecutionRoleArn, Tags = Tags, VpcConfig = VpcConfig, EnableNetworkIsolation = EnableNetworkIsolation)
  output <- .sagemaker$create_model_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model <- sagemaker_create_model

#' Creates the definition for a model bias job
#'
#' @description
#' Creates the definition for a model bias job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_bias_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_bias_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the bias job definition. The name must be unique within an
#' Amazon Web Services Region in the Amazon Web Services account.
#' @param ModelBiasBaselineConfig The baseline configuration for a model bias job.
#' @param ModelBiasAppSpecification &#91;required&#93; Configures the model bias job to run a specified Docker container image.
#' @param ModelBiasJobInput &#91;required&#93; Inputs for the model bias job.
#' @param ModelBiasJobOutputConfig &#91;required&#93; 
#' @param JobResources &#91;required&#93; 
#' @param NetworkConfig Networking options for a model bias job.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
#' assume to perform tasks on your behalf.
#' @param StoppingCondition 
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_bias_job_definition
sagemaker_create_model_bias_job_definition <- function(JobDefinitionName, ModelBiasBaselineConfig = NULL, ModelBiasAppSpecification, ModelBiasJobInput, ModelBiasJobOutputConfig, JobResources, NetworkConfig = NULL, RoleArn, StoppingCondition = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateModelBiasJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_bias_job_definition_input(JobDefinitionName = JobDefinitionName, ModelBiasBaselineConfig = ModelBiasBaselineConfig, ModelBiasAppSpecification = ModelBiasAppSpecification, ModelBiasJobInput = ModelBiasJobInput, ModelBiasJobOutputConfig = ModelBiasJobOutputConfig, JobResources = JobResources, NetworkConfig = NetworkConfig, RoleArn = RoleArn, StoppingCondition = StoppingCondition, Tags = Tags)
  output <- .sagemaker$create_model_bias_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_bias_job_definition <- sagemaker_create_model_bias_job_definition

#' Creates an Amazon SageMaker Model Card
#'
#' @description
#' Creates an Amazon SageMaker Model Card.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_card/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_card/) for full documentation.
#'
#' @param ModelCardName &#91;required&#93; The unique name of the model card.
#' @param SecurityConfig An optional Key Management Service key to encrypt, decrypt, and
#' re-encrypt model card content for regulated workloads with highly
#' sensitive data.
#' @param Content &#91;required&#93; The content of the model card. Content must be in [model card JSON
#' schema](https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html#model-cards-json-schema)
#' and provided as a string.
#' @param ModelCardStatus &#91;required&#93; The approval status of the model card within your organization.
#' Different organizations might have different criteria for model card
#' review and approval.
#' 
#' -   `Draft`: The model card is a work in progress.
#' 
#' -   `PendingReview`: The model card is pending review.
#' 
#' -   `Approved`: The model card is approved.
#' 
#' -   `Archived`: The model card is archived. No more updates should be
#'     made to the model card, but it can still be exported.
#' @param Tags Key-value pairs used to manage metadata for model cards.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_card
sagemaker_create_model_card <- function(ModelCardName, SecurityConfig = NULL, Content, ModelCardStatus, Tags = NULL) {
  op <- new_operation(
    name = "CreateModelCard",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_card_input(ModelCardName = ModelCardName, SecurityConfig = SecurityConfig, Content = Content, ModelCardStatus = ModelCardStatus, Tags = Tags)
  output <- .sagemaker$create_model_card_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_card <- sagemaker_create_model_card

#' Creates an Amazon SageMaker Model Card export job
#'
#' @description
#' Creates an Amazon SageMaker Model Card export job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_card_export_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_card_export_job/) for full documentation.
#'
#' @param ModelCardName &#91;required&#93; The name or Amazon Resource Name (ARN) of the model card to export.
#' @param ModelCardVersion The version of the model card to export. If a version is not provided,
#' then the latest version of the model card is exported.
#' @param ModelCardExportJobName &#91;required&#93; The name of the model card export job.
#' @param OutputConfig &#91;required&#93; The model card output configuration that specifies the Amazon S3 path
#' for exporting.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_card_export_job
sagemaker_create_model_card_export_job <- function(ModelCardName, ModelCardVersion = NULL, ModelCardExportJobName, OutputConfig) {
  op <- new_operation(
    name = "CreateModelCardExportJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_card_export_job_input(ModelCardName = ModelCardName, ModelCardVersion = ModelCardVersion, ModelCardExportJobName = ModelCardExportJobName, OutputConfig = OutputConfig)
  output <- .sagemaker$create_model_card_export_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_card_export_job <- sagemaker_create_model_card_export_job

#' Creates the definition for a model explainability job
#'
#' @description
#' Creates the definition for a model explainability job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_explainability_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_explainability_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the model explainability job definition. The name must be
#' unique within an Amazon Web Services Region in the Amazon Web Services
#' account.
#' @param ModelExplainabilityBaselineConfig The baseline configuration for a model explainability job.
#' @param ModelExplainabilityAppSpecification &#91;required&#93; Configures the model explainability job to run a specified Docker
#' container image.
#' @param ModelExplainabilityJobInput &#91;required&#93; Inputs for the model explainability job.
#' @param ModelExplainabilityJobOutputConfig &#91;required&#93; 
#' @param JobResources &#91;required&#93; 
#' @param NetworkConfig Networking options for a model explainability job.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
#' assume to perform tasks on your behalf.
#' @param StoppingCondition 
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_explainability_job_definition
sagemaker_create_model_explainability_job_definition <- function(JobDefinitionName, ModelExplainabilityBaselineConfig = NULL, ModelExplainabilityAppSpecification, ModelExplainabilityJobInput, ModelExplainabilityJobOutputConfig, JobResources, NetworkConfig = NULL, RoleArn, StoppingCondition = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateModelExplainabilityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_explainability_job_definition_input(JobDefinitionName = JobDefinitionName, ModelExplainabilityBaselineConfig = ModelExplainabilityBaselineConfig, ModelExplainabilityAppSpecification = ModelExplainabilityAppSpecification, ModelExplainabilityJobInput = ModelExplainabilityJobInput, ModelExplainabilityJobOutputConfig = ModelExplainabilityJobOutputConfig, JobResources = JobResources, NetworkConfig = NetworkConfig, RoleArn = RoleArn, StoppingCondition = StoppingCondition, Tags = Tags)
  output <- .sagemaker$create_model_explainability_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_explainability_job_definition <- sagemaker_create_model_explainability_job_definition

#' Creates a model package that you can use to create SageMaker models or
#' list on Amazon Web Services Marketplace, or a versioned model that is
#' part of a model group
#'
#' @description
#' Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_package/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_package/) for full documentation.
#'
#' @param ModelPackageName The name of the model package. The name must have 1 to 63 characters.
#' Valid characters are a-z, A-Z, 0-9, and - (hyphen).
#' 
#' This parameter is required for unversioned models. It is not applicable
#' to versioned models.
#' @param ModelPackageGroupName The name or Amazon Resource Name (ARN) of the model package group that
#' this model version belongs to.
#' 
#' This parameter is required for versioned models, and does not apply to
#' unversioned models.
#' @param ModelPackageDescription A description of the model package.
#' @param InferenceSpecification Specifies details about inference jobs that can be run with models based
#' on this model package, including the following:
#' 
#' -   The Amazon ECR paths of containers that contain the inference code
#'     and model artifacts.
#' 
#' -   The instance types that the model package supports for transform
#'     jobs and real-time endpoints used for inference.
#' 
#' -   The input and output content formats that the model package supports
#'     for inference.
#' @param ValidationSpecification Specifies configurations for one or more transform jobs that SageMaker
#' runs to test the model package.
#' @param SourceAlgorithmSpecification Details about the algorithm that was used to create the model package.
#' @param CertifyForMarketplace Whether to certify the model package for listing on Amazon Web Services
#' Marketplace.
#' 
#' This parameter is optional for unversioned models, and does not apply to
#' versioned models.
#' @param Tags A list of key value pairs associated with the model. For more
#' information, see [Tagging Amazon Web Services
#' resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html)
#' in the *Amazon Web Services General Reference Guide*.
#' 
#' If you supply `ModelPackageGroupName`, your model package belongs to the
#' model group you specify and uses the tags associated with the model
#' group. In this case, you cannot supply a `tag` argument.
#' @param ModelApprovalStatus Whether the model is approved for deployment.
#' 
#' This parameter is optional for versioned models, and does not apply to
#' unversioned models.
#' 
#' For versioned models, the value of this parameter must be set to
#' `Approved` to deploy the model.
#' @param MetadataProperties 
#' @param ModelMetrics A structure that contains model metrics reports.
#' @param ClientToken A unique token that guarantees that the call to this API is idempotent.
#' @param CustomerMetadataProperties The metadata properties associated with the model package versions.
#' @param DriftCheckBaselines Represents the drift check baselines that can be used when the model
#' monitor is set using the model package. For more information, see the
#' topic on [Drift Detection against Previous Baselines in SageMaker
#' Pipelines](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-quality-clarify-baseline-lifecycle.html#pipelines-quality-clarify-baseline-drift-detection)
#' in the *Amazon SageMaker Developer Guide*.
#' @param Domain The machine learning domain of your model package and its components.
#' Common machine learning domains include computer vision and natural
#' language processing.
#' @param Task The machine learning task your model package accomplishes. Common
#' machine learning tasks include object detection and image
#' classification. The following tasks are supported by Inference
#' Recommender: `"IMAGE_CLASSIFICATION"` | `"OBJECT_DETECTION"` |
#' `"TEXT_GENERATION"` |`"IMAGE_SEGMENTATION"` | `"FILL_MASK"` |
#' `"CLASSIFICATION"` | `"REGRESSION"` | `"OTHER"`.
#' 
#' Specify "OTHER" if none of the tasks listed fit your use case.
#' @param SamplePayloadUrl The Amazon Simple Storage Service (Amazon S3) path where the sample
#' payload is stored. This path must point to a single gzip compressed tar
#' archive (.tar.gz suffix). This archive can hold multiple files that are
#' all equally used in the load test. Each file in the archive must satisfy
#' the size constraints of the
#' [InvokeEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html#API_runtime_InvokeEndpoint_RequestSyntax)
#' call.
#' @param AdditionalInferenceSpecifications An array of additional Inference Specification objects. Each additional
#' Inference Specification specifies artifacts based on this model package
#' that can be used on inference endpoints. Generally used with SageMaker
#' Neo to store the compiled artifacts.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_package
sagemaker_create_model_package <- function(ModelPackageName = NULL, ModelPackageGroupName = NULL, ModelPackageDescription = NULL, InferenceSpecification = NULL, ValidationSpecification = NULL, SourceAlgorithmSpecification = NULL, CertifyForMarketplace = NULL, Tags = NULL, ModelApprovalStatus = NULL, MetadataProperties = NULL, ModelMetrics = NULL, ClientToken = NULL, CustomerMetadataProperties = NULL, DriftCheckBaselines = NULL, Domain = NULL, Task = NULL, SamplePayloadUrl = NULL, AdditionalInferenceSpecifications = NULL) {
  op <- new_operation(
    name = "CreateModelPackage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_package_input(ModelPackageName = ModelPackageName, ModelPackageGroupName = ModelPackageGroupName, ModelPackageDescription = ModelPackageDescription, InferenceSpecification = InferenceSpecification, ValidationSpecification = ValidationSpecification, SourceAlgorithmSpecification = SourceAlgorithmSpecification, CertifyForMarketplace = CertifyForMarketplace, Tags = Tags, ModelApprovalStatus = ModelApprovalStatus, MetadataProperties = MetadataProperties, ModelMetrics = ModelMetrics, ClientToken = ClientToken, CustomerMetadataProperties = CustomerMetadataProperties, DriftCheckBaselines = DriftCheckBaselines, Domain = Domain, Task = Task, SamplePayloadUrl = SamplePayloadUrl, AdditionalInferenceSpecifications = AdditionalInferenceSpecifications)
  output <- .sagemaker$create_model_package_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_package <- sagemaker_create_model_package

#' Creates a model group
#'
#' @description
#' Creates a model group. A model group contains a group of model versions.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_package_group/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_package_group/) for full documentation.
#'
#' @param ModelPackageGroupName &#91;required&#93; The name of the model group.
#' @param ModelPackageGroupDescription A description for the model group.
#' @param Tags A list of key value pairs associated with the model group. For more
#' information, see [Tagging Amazon Web Services
#' resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html)
#' in the *Amazon Web Services General Reference Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_package_group
sagemaker_create_model_package_group <- function(ModelPackageGroupName, ModelPackageGroupDescription = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateModelPackageGroup",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_package_group_input(ModelPackageGroupName = ModelPackageGroupName, ModelPackageGroupDescription = ModelPackageGroupDescription, Tags = Tags)
  output <- .sagemaker$create_model_package_group_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_package_group <- sagemaker_create_model_package_group

#' Creates a definition for a job that monitors model quality and drift
#'
#' @description
#' Creates a definition for a job that monitors model quality and drift. For information about model monitor, see [Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_model_quality_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_create_model_quality_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the monitoring job definition.
#' @param ModelQualityBaselineConfig Specifies the constraints and baselines for the monitoring job.
#' @param ModelQualityAppSpecification &#91;required&#93; The container that runs the monitoring job.
#' @param ModelQualityJobInput &#91;required&#93; A list of the inputs that are monitored. Currently endpoints are
#' supported.
#' @param ModelQualityJobOutputConfig &#91;required&#93; 
#' @param JobResources &#91;required&#93; 
#' @param NetworkConfig Specifies the network configuration for the monitoring job.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
#' assume to perform tasks on your behalf.
#' @param StoppingCondition 
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_model_quality_job_definition
sagemaker_create_model_quality_job_definition <- function(JobDefinitionName, ModelQualityBaselineConfig = NULL, ModelQualityAppSpecification, ModelQualityJobInput, ModelQualityJobOutputConfig, JobResources, NetworkConfig = NULL, RoleArn, StoppingCondition = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateModelQualityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_model_quality_job_definition_input(JobDefinitionName = JobDefinitionName, ModelQualityBaselineConfig = ModelQualityBaselineConfig, ModelQualityAppSpecification = ModelQualityAppSpecification, ModelQualityJobInput = ModelQualityJobInput, ModelQualityJobOutputConfig = ModelQualityJobOutputConfig, JobResources = JobResources, NetworkConfig = NetworkConfig, RoleArn = RoleArn, StoppingCondition = StoppingCondition, Tags = Tags)
  output <- .sagemaker$create_model_quality_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_model_quality_job_definition <- sagemaker_create_model_quality_job_definition

#' Creates a schedule that regularly starts Amazon SageMaker Processing
#' Jobs to monitor the data captured for an Amazon SageMaker Endpoint
#'
#' @description
#' Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_monitoring_schedule/](https://www.paws-r-sdk.com/docs/sagemaker_create_monitoring_schedule/) for full documentation.
#'
#' @param MonitoringScheduleName &#91;required&#93; The name of the monitoring schedule. The name must be unique within an
#' Amazon Web Services Region within an Amazon Web Services account.
#' @param MonitoringScheduleConfig &#91;required&#93; The configuration object that specifies the monitoring schedule and
#' defines the monitoring job.
#' @param Tags (Optional) An array of key-value pairs. For more information, see Using
#' Cost Allocation Tags in the *Amazon Web Services Billing and Cost
#' Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_monitoring_schedule
sagemaker_create_monitoring_schedule <- function(MonitoringScheduleName, MonitoringScheduleConfig, Tags = NULL) {
  op <- new_operation(
    name = "CreateMonitoringSchedule",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_monitoring_schedule_input(MonitoringScheduleName = MonitoringScheduleName, MonitoringScheduleConfig = MonitoringScheduleConfig, Tags = Tags)
  output <- .sagemaker$create_monitoring_schedule_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_monitoring_schedule <- sagemaker_create_monitoring_schedule

#' Creates an SageMaker notebook instance
#'
#' @description
#' Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_notebook_instance/](https://www.paws-r-sdk.com/docs/sagemaker_create_notebook_instance/) for full documentation.
#'
#' @param NotebookInstanceName &#91;required&#93; The name of the new notebook instance.
#' @param InstanceType &#91;required&#93; The type of ML compute instance to launch for the notebook instance.
#' @param SubnetId The ID of the subnet in a VPC to which you would like to have a
#' connectivity from your ML compute instance.
#' @param SecurityGroupIds The VPC security group IDs, in the form sg-xxxxxxxx. The security groups
#' must be for the same VPC as specified in the subnet.
#' @param RoleArn &#91;required&#93; When you send any requests to Amazon Web Services resources from the
#' notebook instance, SageMaker assumes this role to perform tasks on your
#' behalf. You must grant this role necessary permissions so SageMaker can
#' perform these tasks. The policy must allow the SageMaker service
#' principal (sagemaker.amazonaws.com) permissions to assume this role. For
#' more information, see [SageMaker
#' Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).
#' 
#' To be able to pass this role to SageMaker, the caller of this API must
#' have the `iam:PassRole` permission.
#' @param KmsKeyId The Amazon Resource Name (ARN) of a Amazon Web Services Key Management
#' Service key that SageMaker uses to encrypt data on the storage volume
#' attached to your notebook instance. The KMS key you provide must be
#' enabled. For information, see [Enabling and Disabling
#' Keys](https://docs.aws.amazon.com/kms/latest/developerguide/enabling-keys.html)
#' in the *Amazon Web Services Key Management Service Developer Guide*.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' @param LifecycleConfigName The name of a lifecycle configuration to associate with the notebook
#' instance. For information about lifestyle configurations, see [Step 2.1:
#' (Optional) Customize a Notebook
#' Instance](https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html).
#' @param DirectInternetAccess Sets whether SageMaker provides internet access to the notebook
#' instance. If you set this to `Disabled` this notebook instance is able
#' to access resources only in your VPC, and is not be able to connect to
#' SageMaker training and endpoint services unless you configure a NAT
#' Gateway in your VPC.
#' 
#' For more information, see [Notebook Instances Are Internet-Enabled by
#' Default](https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html#appendix-notebook-and-internet-access).
#' You can set the value of this parameter to `Disabled` only if you set a
#' value for the `SubnetId` parameter.
#' @param VolumeSizeInGB The size, in GB, of the ML storage volume to attach to the notebook
#' instance. The default value is 5 GB.
#' @param AcceleratorTypes A list of Elastic Inference (EI) instance types to associate with this
#' notebook instance. Currently, only one instance type can be associated
#' with a notebook instance. For more information, see [Using Elastic
#' Inference in Amazon
#' SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html).
#' @param DefaultCodeRepository A Git repository to associate with the notebook instance as its default
#' code repository. This can be either the name of a Git repository stored
#' as a resource in your account, or the URL of a Git repository in [Amazon
#' Web Services
#' CodeCommit](https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html)
#' or in any other Git repository. When you open a notebook instance, it
#' opens in the directory that contains this repository. For more
#' information, see [Associating Git Repositories with SageMaker Notebook
#' Instances](https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html).
#' @param AdditionalCodeRepositories An array of up to three Git repositories to associate with the notebook
#' instance. These can be either the names of Git repositories stored as
#' resources in your account, or the URL of Git repositories in [Amazon Web
#' Services
#' CodeCommit](https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html)
#' or in any other Git repository. These repositories are cloned at the
#' same level as the default repository of your notebook instance. For more
#' information, see [Associating Git Repositories with SageMaker Notebook
#' Instances](https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html).
#' @param RootAccess Whether root access is enabled or disabled for users of the notebook
#' instance. The default value is `Enabled`.
#' 
#' Lifecycle configurations need root access to be able to set up a
#' notebook instance. Because of this, lifecycle configurations associated
#' with a notebook instance always run with root access even if you disable
#' root access for users.
#' @param PlatformIdentifier The platform identifier of the notebook instance runtime environment.
#' @param InstanceMetadataServiceConfiguration Information on the IMDS configuration of the notebook instance
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_notebook_instance
sagemaker_create_notebook_instance <- function(NotebookInstanceName, InstanceType, SubnetId = NULL, SecurityGroupIds = NULL, RoleArn, KmsKeyId = NULL, Tags = NULL, LifecycleConfigName = NULL, DirectInternetAccess = NULL, VolumeSizeInGB = NULL, AcceleratorTypes = NULL, DefaultCodeRepository = NULL, AdditionalCodeRepositories = NULL, RootAccess = NULL, PlatformIdentifier = NULL, InstanceMetadataServiceConfiguration = NULL) {
  op <- new_operation(
    name = "CreateNotebookInstance",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_notebook_instance_input(NotebookInstanceName = NotebookInstanceName, InstanceType = InstanceType, SubnetId = SubnetId, SecurityGroupIds = SecurityGroupIds, RoleArn = RoleArn, KmsKeyId = KmsKeyId, Tags = Tags, LifecycleConfigName = LifecycleConfigName, DirectInternetAccess = DirectInternetAccess, VolumeSizeInGB = VolumeSizeInGB, AcceleratorTypes = AcceleratorTypes, DefaultCodeRepository = DefaultCodeRepository, AdditionalCodeRepositories = AdditionalCodeRepositories, RootAccess = RootAccess, PlatformIdentifier = PlatformIdentifier, InstanceMetadataServiceConfiguration = InstanceMetadataServiceConfiguration)
  output <- .sagemaker$create_notebook_instance_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_notebook_instance <- sagemaker_create_notebook_instance

#' Creates a lifecycle configuration that you can associate with a notebook
#' instance
#'
#' @description
#' Creates a lifecycle configuration that you can associate with a notebook instance. A *lifecycle configuration* is a collection of shell scripts that run when you create or start a notebook instance.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_notebook_instance_lifecycle_config/](https://www.paws-r-sdk.com/docs/sagemaker_create_notebook_instance_lifecycle_config/) for full documentation.
#'
#' @param NotebookInstanceLifecycleConfigName &#91;required&#93; The name of the lifecycle configuration.
#' @param OnCreate A shell script that runs only once, when you create a notebook instance.
#' The shell script must be a base64-encoded string.
#' @param OnStart A shell script that runs every time you start a notebook instance,
#' including when you create the notebook instance. The shell script must
#' be a base64-encoded string.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_notebook_instance_lifecycle_config
sagemaker_create_notebook_instance_lifecycle_config <- function(NotebookInstanceLifecycleConfigName, OnCreate = NULL, OnStart = NULL) {
  op <- new_operation(
    name = "CreateNotebookInstanceLifecycleConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_notebook_instance_lifecycle_config_input(NotebookInstanceLifecycleConfigName = NotebookInstanceLifecycleConfigName, OnCreate = OnCreate, OnStart = OnStart)
  output <- .sagemaker$create_notebook_instance_lifecycle_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_notebook_instance_lifecycle_config <- sagemaker_create_notebook_instance_lifecycle_config

#' Creates a pipeline using a JSON pipeline definition
#'
#' @description
#' Creates a pipeline using a JSON pipeline definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_pipeline/](https://www.paws-r-sdk.com/docs/sagemaker_create_pipeline/) for full documentation.
#'
#' @param PipelineName &#91;required&#93; The name of the pipeline.
#' @param PipelineDisplayName The display name of the pipeline.
#' @param PipelineDefinition The [JSON pipeline
#' definition](https://aws-sagemaker-mlops.github.io/sagemaker-model-building-pipeline-definition-JSON-schema/)
#' of the pipeline.
#' @param PipelineDefinitionS3Location The location of the pipeline definition stored in Amazon S3. If
#' specified, SageMaker will retrieve the pipeline definition from this
#' location.
#' @param PipelineDescription A description of the pipeline.
#' @param ClientRequestToken &#91;required&#93; A unique, case-sensitive identifier that you provide to ensure the
#' idempotency of the operation. An idempotent operation completes no more
#' than one time.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of the role used by the pipeline to
#' access and create resources.
#' @param Tags A list of tags to apply to the created pipeline.
#' @param ParallelismConfiguration This is the configuration that controls the parallelism of the pipeline.
#' If specified, it applies to all runs of this pipeline by default.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_pipeline
sagemaker_create_pipeline <- function(PipelineName, PipelineDisplayName = NULL, PipelineDefinition = NULL, PipelineDefinitionS3Location = NULL, PipelineDescription = NULL, ClientRequestToken, RoleArn, Tags = NULL, ParallelismConfiguration = NULL) {
  op <- new_operation(
    name = "CreatePipeline",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_pipeline_input(PipelineName = PipelineName, PipelineDisplayName = PipelineDisplayName, PipelineDefinition = PipelineDefinition, PipelineDefinitionS3Location = PipelineDefinitionS3Location, PipelineDescription = PipelineDescription, ClientRequestToken = ClientRequestToken, RoleArn = RoleArn, Tags = Tags, ParallelismConfiguration = ParallelismConfiguration)
  output <- .sagemaker$create_pipeline_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_pipeline <- sagemaker_create_pipeline

#' Creates a URL for a specified UserProfile in a Domain
#'
#' @description
#' Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_presigned_domain_url/](https://www.paws-r-sdk.com/docs/sagemaker_create_presigned_domain_url/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param UserProfileName &#91;required&#93; The name of the UserProfile to sign-in as.
#' @param SessionExpirationDurationInSeconds The session expiration duration in seconds. This value defaults to
#' 43200.
#' @param ExpiresInSeconds The number of seconds until the pre-signed URL expires. This value
#' defaults to 300.
#' @param SpaceName The name of the space.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_presigned_domain_url
sagemaker_create_presigned_domain_url <- function(DomainId, UserProfileName, SessionExpirationDurationInSeconds = NULL, ExpiresInSeconds = NULL, SpaceName = NULL) {
  op <- new_operation(
    name = "CreatePresignedDomainUrl",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_presigned_domain_url_input(DomainId = DomainId, UserProfileName = UserProfileName, SessionExpirationDurationInSeconds = SessionExpirationDurationInSeconds, ExpiresInSeconds = ExpiresInSeconds, SpaceName = SpaceName)
  output <- .sagemaker$create_presigned_domain_url_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_presigned_domain_url <- sagemaker_create_presigned_domain_url

#' Returns a URL that you can use to connect to the Jupyter server from a
#' notebook instance
#'
#' @description
#' Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose `Open` next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_presigned_notebook_instance_url/](https://www.paws-r-sdk.com/docs/sagemaker_create_presigned_notebook_instance_url/) for full documentation.
#'
#' @param NotebookInstanceName &#91;required&#93; The name of the notebook instance.
#' @param SessionExpirationDurationInSeconds The duration of the session, in seconds. The default is 12 hours.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_presigned_notebook_instance_url
sagemaker_create_presigned_notebook_instance_url <- function(NotebookInstanceName, SessionExpirationDurationInSeconds = NULL) {
  op <- new_operation(
    name = "CreatePresignedNotebookInstanceUrl",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_presigned_notebook_instance_url_input(NotebookInstanceName = NotebookInstanceName, SessionExpirationDurationInSeconds = SessionExpirationDurationInSeconds)
  output <- .sagemaker$create_presigned_notebook_instance_url_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_presigned_notebook_instance_url <- sagemaker_create_presigned_notebook_instance_url

#' Creates a processing job
#'
#' @description
#' Creates a processing job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_processing_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_processing_job/) for full documentation.
#'
#' @param ProcessingInputs An array of inputs configuring the data to download into the processing
#' container.
#' @param ProcessingOutputConfig Output configuration for the processing job.
#' @param ProcessingJobName &#91;required&#93; The name of the processing job. The name must be unique within an Amazon
#' Web Services Region in the Amazon Web Services account.
#' @param ProcessingResources &#91;required&#93; Identifies the resources, ML compute instances, and ML storage volumes
#' to deploy for a processing job. In distributed training, you specify
#' more than one instance.
#' @param StoppingCondition The time limit for how long the processing job is allowed to run.
#' @param AppSpecification &#91;required&#93; Configures the processing job to run a specified Docker container image.
#' @param Environment The environment variables to set in the Docker container. Up to 100 key
#' and values entries in the map are supported.
#' @param NetworkConfig Networking options for a processing job, such as whether to allow
#' inbound and outbound network calls to and from processing containers,
#' and the VPC subnets and security groups to use for VPC-enabled
#' processing jobs.
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
#' assume to perform tasks on your behalf.
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#' @param ExperimentConfig 
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_processing_job
sagemaker_create_processing_job <- function(ProcessingInputs = NULL, ProcessingOutputConfig = NULL, ProcessingJobName, ProcessingResources, StoppingCondition = NULL, AppSpecification, Environment = NULL, NetworkConfig = NULL, RoleArn, Tags = NULL, ExperimentConfig = NULL) {
  op <- new_operation(
    name = "CreateProcessingJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_processing_job_input(ProcessingInputs = ProcessingInputs, ProcessingOutputConfig = ProcessingOutputConfig, ProcessingJobName = ProcessingJobName, ProcessingResources = ProcessingResources, StoppingCondition = StoppingCondition, AppSpecification = AppSpecification, Environment = Environment, NetworkConfig = NetworkConfig, RoleArn = RoleArn, Tags = Tags, ExperimentConfig = ExperimentConfig)
  output <- .sagemaker$create_processing_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_processing_job <- sagemaker_create_processing_job

#' Creates a machine learning (ML) project that can contain one or more
#' templates that set up an ML pipeline from training to deploying an
#' approved model
#'
#' @description
#' Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_project/](https://www.paws-r-sdk.com/docs/sagemaker_create_project/) for full documentation.
#'
#' @param ProjectName &#91;required&#93; The name of the project.
#' @param ProjectDescription A description for the project.
#' @param ServiceCatalogProvisioningDetails &#91;required&#93; The product ID and provisioning artifact ID to provision a service
#' catalog. The provisioning artifact ID will default to the latest
#' provisioning artifact ID of the product, if you don't provide the
#' provisioning artifact ID. For more information, see [What is Amazon Web
#' Services Service
#' Catalog](https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html).
#' @param Tags An array of key-value pairs that you want to use to organize and track
#' your Amazon Web Services resource costs. For more information, see
#' [Tagging Amazon Web Services
#' resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html)
#' in the *Amazon Web Services General Reference Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_project
sagemaker_create_project <- function(ProjectName, ProjectDescription = NULL, ServiceCatalogProvisioningDetails, Tags = NULL) {
  op <- new_operation(
    name = "CreateProject",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_project_input(ProjectName = ProjectName, ProjectDescription = ProjectDescription, ServiceCatalogProvisioningDetails = ServiceCatalogProvisioningDetails, Tags = Tags)
  output <- .sagemaker$create_project_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_project <- sagemaker_create_project

#' Creates a space used for real time collaboration in a Domain
#'
#' @description
#' Creates a space used for real time collaboration in a Domain.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_space/](https://www.paws-r-sdk.com/docs/sagemaker_create_space/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The ID of the associated Domain.
#' @param SpaceName &#91;required&#93; The name of the space.
#' @param Tags Tags to associated with the space. Each tag consists of a key and an
#' optional value. Tag keys must be unique for each resource. Tags are
#' searchable using the [`search`][sagemaker_search] API.
#' @param SpaceSettings A collection of space settings.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_space
sagemaker_create_space <- function(DomainId, SpaceName, Tags = NULL, SpaceSettings = NULL) {
  op <- new_operation(
    name = "CreateSpace",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_space_input(DomainId = DomainId, SpaceName = SpaceName, Tags = Tags, SpaceSettings = SpaceSettings)
  output <- .sagemaker$create_space_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_space <- sagemaker_create_space

#' Creates a new Studio Lifecycle Configuration
#'
#' @description
#' Creates a new Studio Lifecycle Configuration.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_studio_lifecycle_config/](https://www.paws-r-sdk.com/docs/sagemaker_create_studio_lifecycle_config/) for full documentation.
#'
#' @param StudioLifecycleConfigName &#91;required&#93; The name of the Studio Lifecycle Configuration to create.
#' @param StudioLifecycleConfigContent &#91;required&#93; The content of your Studio Lifecycle Configuration script. This content
#' must be base64 encoded.
#' @param StudioLifecycleConfigAppType &#91;required&#93; The App type that the Lifecycle Configuration is attached to.
#' @param Tags Tags to be associated with the Lifecycle Configuration. Each tag
#' consists of a key and an optional value. Tag keys must be unique per
#' resource. Tags are searchable using the Search API.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_studio_lifecycle_config
sagemaker_create_studio_lifecycle_config <- function(StudioLifecycleConfigName, StudioLifecycleConfigContent, StudioLifecycleConfigAppType, Tags = NULL) {
  op <- new_operation(
    name = "CreateStudioLifecycleConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_studio_lifecycle_config_input(StudioLifecycleConfigName = StudioLifecycleConfigName, StudioLifecycleConfigContent = StudioLifecycleConfigContent, StudioLifecycleConfigAppType = StudioLifecycleConfigAppType, Tags = Tags)
  output <- .sagemaker$create_studio_lifecycle_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_studio_lifecycle_config <- sagemaker_create_studio_lifecycle_config

#' Starts a model training job
#'
#' @description
#' Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_training_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_training_job/) for full documentation.
#'
#' @param TrainingJobName &#91;required&#93; The name of the training job. The name must be unique within an Amazon
#' Web Services Region in an Amazon Web Services account.
#' @param HyperParameters Algorithm-specific parameters that influence the quality of the model.
#' You set hyperparameters before you start the learning process. For a
#' list of hyperparameters for each training algorithm provided by
#' SageMaker, see
#' [Algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
#' 
#' You can specify a maximum of 100 hyperparameters. Each hyperparameter is
#' a key-value pair. Each key and value is limited to 256 characters, as
#' specified by the `Length Constraint`.
#' 
#' Do not include any security-sensitive information including account
#' access IDs, secrets or tokens in any hyperparameter field. If the use of
#' security-sensitive credentials are detected, SageMaker will reject your
#' training job request and return an exception error.
#' @param AlgorithmSpecification &#91;required&#93; The registry path of the Docker image that contains the training
#' algorithm and algorithm-specific metadata, including the input mode. For
#' more information about algorithms provided by SageMaker, see
#' [Algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
#' For information about providing your own algorithms, see [Using Your Own
#' Algorithms with Amazon
#' SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers.html).
#' @param RoleArn &#91;required&#93; The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume
#' to perform tasks on your behalf.
#' 
#' During model training, SageMaker needs your permission to read input
#' data from an S3 bucket, download a Docker image that contains training
#' code, write model artifacts to an S3 bucket, write logs to Amazon
#' CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant
#' permissions for all of these tasks to an IAM role. For more information,
#' see [SageMaker
#' Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).
#' 
#' To be able to pass this role to SageMaker, the caller of this API must
#' have the `iam:PassRole` permission.
#' @param InputDataConfig An array of `Channel` objects. Each channel is a named input source.
#' `InputDataConfig` describes the input data and its location.
#' 
#' Algorithms can accept input data from one or more channels. For example,
#' an algorithm might have two channels of input data, `training_data` and
#' `validation_data`. The configuration for each channel provides the S3,
#' EFS, or FSx location where the input data is stored. It also provides
#' information about the stored data: the MIME type, compression method,
#' and whether the data is wrapped in RecordIO format.
#' 
#' Depending on the input mode that the algorithm supports, SageMaker
#' either copies input data files from an S3 bucket to a local directory in
#' the Docker container, or makes it available as input streams. For
#' example, if you specify an EFS location, input data files are available
#' as input streams. They do not need to be downloaded.
#' 
#' Your input must be in the same Amazon Web Services region as your
#' training job.
#' @param OutputDataConfig &#91;required&#93; Specifies the path to the S3 location where you want to store model
#' artifacts. SageMaker creates subfolders for the artifacts.
#' @param ResourceConfig &#91;required&#93; The resources, including the ML compute instances and ML storage
#' volumes, to use for model training.
#' 
#' ML storage volumes store model artifacts and incremental states.
#' Training algorithms might also use ML storage volumes for scratch space.
#' If you want SageMaker to use the ML storage volume to store the training
#' data, choose `File` as the `TrainingInputMode` in the algorithm
#' specification. For distributed training algorithms, specify an instance
#' count greater than 1.
#' @param VpcConfig A
#' [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html)
#' object that specifies the VPC that you want your training job to connect
#' to. Control access to and from your training container by configuring
#' the VPC. For more information, see [Protect Training Jobs by Using an
#' Amazon Virtual Private
#' Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
#' @param StoppingCondition &#91;required&#93; Specifies a limit to how long a model training job can run. It also
#' specifies how long a managed Spot training job has to complete. When the
#' job reaches the time limit, SageMaker ends the training job. Use this
#' API to cap model training costs.
#' 
#' To stop a job, SageMaker sends the algorithm the `SIGTERM` signal, which
#' delays job termination for 120 seconds. Algorithms can use this
#' 120-second window to save the model artifacts, so the results of
#' training are not lost.
#' @param Tags An array of key-value pairs. You can use tags to categorize your Amazon
#' Web Services resources in different ways, for example, by purpose,
#' owner, or environment. For more information, see [Tagging Amazon Web
#' Services
#' Resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
#' @param EnableNetworkIsolation Isolates the training container. No inbound or outbound network calls
#' can be made, except for calls between peers within a training cluster
#' for distributed training. If you enable network isolation for training
#' jobs that are configured to use a VPC, SageMaker downloads and uploads
#' customer data and model artifacts through the specified VPC, but the
#' training container does not have network access.
#' @param EnableInterContainerTrafficEncryption To encrypt all communications between ML compute instances in
#' distributed training, choose `True`. Encryption provides greater
#' security for distributed training, but training might take longer. How
#' long it takes depends on the amount of communication between compute
#' instances, especially if you use a deep learning algorithm in
#' distributed training. For more information, see [Protect Communications
#' Between ML Compute Instances in a Distributed Training
#' Job](https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html).
#' @param EnableManagedSpotTraining To train models using managed spot training, choose `True`. Managed spot
#' training provides a fully managed and scalable infrastructure for
#' training machine learning models. this option is useful when training
#' jobs can be interrupted and when there is flexibility when the training
#' job is run.
#' 
#' The complete and intermediate results of jobs are stored in an Amazon S3
#' bucket, and can be used as a starting point to train models
#' incrementally. Amazon SageMaker provides metrics and logs in CloudWatch.
#' They can be used to see when managed spot training jobs are running,
#' interrupted, resumed, or completed.
#' @param CheckpointConfig Contains information about the output location for managed spot training
#' checkpoint data.
#' @param DebugHookConfig 
#' @param DebugRuleConfigurations Configuration information for Amazon SageMaker Debugger rules for
#' debugging output tensors.
#' @param TensorBoardOutputConfig 
#' @param ExperimentConfig 
#' @param ProfilerConfig 
#' @param ProfilerRuleConfigurations Configuration information for Amazon SageMaker Debugger rules for
#' profiling system and framework metrics.
#' @param Environment The environment variables to set in the Docker container.
#' @param RetryStrategy The number of times to retry the job when the job fails due to an
#' `InternalServerError`.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_training_job
sagemaker_create_training_job <- function(TrainingJobName, HyperParameters = NULL, AlgorithmSpecification, RoleArn, InputDataConfig = NULL, OutputDataConfig, ResourceConfig, VpcConfig = NULL, StoppingCondition, Tags = NULL, EnableNetworkIsolation = NULL, EnableInterContainerTrafficEncryption = NULL, EnableManagedSpotTraining = NULL, CheckpointConfig = NULL, DebugHookConfig = NULL, DebugRuleConfigurations = NULL, TensorBoardOutputConfig = NULL, ExperimentConfig = NULL, ProfilerConfig = NULL, ProfilerRuleConfigurations = NULL, Environment = NULL, RetryStrategy = NULL) {
  op <- new_operation(
    name = "CreateTrainingJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_training_job_input(TrainingJobName = TrainingJobName, HyperParameters = HyperParameters, AlgorithmSpecification = AlgorithmSpecification, RoleArn = RoleArn, InputDataConfig = InputDataConfig, OutputDataConfig = OutputDataConfig, ResourceConfig = ResourceConfig, VpcConfig = VpcConfig, StoppingCondition = StoppingCondition, Tags = Tags, EnableNetworkIsolation = EnableNetworkIsolation, EnableInterContainerTrafficEncryption = EnableInterContainerTrafficEncryption, EnableManagedSpotTraining = EnableManagedSpotTraining, CheckpointConfig = CheckpointConfig, DebugHookConfig = DebugHookConfig, DebugRuleConfigurations = DebugRuleConfigurations, TensorBoardOutputConfig = TensorBoardOutputConfig, ExperimentConfig = ExperimentConfig, ProfilerConfig = ProfilerConfig, ProfilerRuleConfigurations = ProfilerRuleConfigurations, Environment = Environment, RetryStrategy = RetryStrategy)
  output <- .sagemaker$create_training_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_training_job <- sagemaker_create_training_job

#' Starts a transform job
#'
#' @description
#' Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_transform_job/](https://www.paws-r-sdk.com/docs/sagemaker_create_transform_job/) for full documentation.
#'
#' @param TransformJobName &#91;required&#93; The name of the transform job. The name must be unique within an Amazon
#' Web Services Region in an Amazon Web Services account.
#' @param ModelName &#91;required&#93; The name of the model that you want to use for the transform job.
#' `ModelName` must be the name of an existing Amazon SageMaker model
#' within an Amazon Web Services Region in an Amazon Web Services account.
#' @param MaxConcurrentTransforms The maximum number of parallel requests that can be sent to each
#' instance in a transform job. If `MaxConcurrentTransforms` is set to `0`
#' or left unset, Amazon SageMaker checks the optional execution-parameters
#' to determine the settings for your chosen algorithm. If the
#' execution-parameters endpoint is not enabled, the default value is `1`.
#' For more information on execution-parameters, see [How Containers Serve
#' Requests](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests).
#' For built-in algorithms, you don't need to set a value for
#' `MaxConcurrentTransforms`.
#' @param ModelClientConfig Configures the timeout and maximum number of retries for processing a
#' transform job invocation.
#' @param MaxPayloadInMB The maximum allowed size of the payload, in MB. A *payload* is the data
#' portion of a record (without metadata). The value in `MaxPayloadInMB`
#' must be greater than, or equal to, the size of a single record. To
#' estimate the size of a record in MB, divide the size of your dataset by
#' the number of records. To ensure that the records fit within the maximum
#' payload size, we recommend using a slightly larger value. The default
#' value is `6` MB.
#' 
#' The value of `MaxPayloadInMB` cannot be greater than 100 MB. If you
#' specify the `MaxConcurrentTransforms` parameter, the value of
#' `(MaxConcurrentTransforms * MaxPayloadInMB)` also cannot exceed 100 MB.
#' 
#' For cases where the payload might be arbitrarily large and is
#' transmitted using HTTP chunked encoding, set the value to `0`. This
#' feature works only in supported algorithms. Currently, Amazon SageMaker
#' built-in algorithms do not support HTTP chunked encoding.
#' @param BatchStrategy Specifies the number of records to include in a mini-batch for an HTTP
#' inference request. A *record* is a single unit of input data that
#' inference can be made on. For example, a single line in a CSV file is a
#' record.
#' 
#' To enable the batch strategy, you must set the `SplitType` property to
#' `Line`, `RecordIO`, or `TFRecord`.
#' 
#' To use only one record when making an HTTP invocation request to a
#' container, set `BatchStrategy` to `SingleRecord` and `SplitType` to
#' `Line`.
#' 
#' To fit as many records in a mini-batch as can fit within the
#' `MaxPayloadInMB` limit, set `BatchStrategy` to `MultiRecord` and
#' `SplitType` to `Line`.
#' @param Environment The environment variables to set in the Docker container. We support up
#' to 16 key and values entries in the map.
#' @param TransformInput &#91;required&#93; Describes the input source and the way the transform job consumes it.
#' @param TransformOutput &#91;required&#93; Describes the results of the transform job.
#' @param DataCaptureConfig Configuration to control how SageMaker captures inference data.
#' @param TransformResources &#91;required&#93; Describes the resources, including ML instance types and ML instance
#' count, to use for the transform job.
#' @param DataProcessing The data structure used to specify the data to be used for inference in
#' a batch transform job and to associate the data that is relevant to the
#' prediction results in the output. The input filter provided allows you
#' to exclude input data that is not needed for inference in a batch
#' transform job. The output filter provided allows you to include input
#' data relevant to interpreting the predictions in the output from the
#' job. For more information, see [Associate Prediction Results with their
#' Corresponding Input
#' Records](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html).
#' @param Tags (Optional) An array of key-value pairs. For more information, see [Using
#' Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#' @param ExperimentConfig 
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_transform_job
sagemaker_create_transform_job <- function(TransformJobName, ModelName, MaxConcurrentTransforms = NULL, ModelClientConfig = NULL, MaxPayloadInMB = NULL, BatchStrategy = NULL, Environment = NULL, TransformInput, TransformOutput, DataCaptureConfig = NULL, TransformResources, DataProcessing = NULL, Tags = NULL, ExperimentConfig = NULL) {
  op <- new_operation(
    name = "CreateTransformJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_transform_job_input(TransformJobName = TransformJobName, ModelName = ModelName, MaxConcurrentTransforms = MaxConcurrentTransforms, ModelClientConfig = ModelClientConfig, MaxPayloadInMB = MaxPayloadInMB, BatchStrategy = BatchStrategy, Environment = Environment, TransformInput = TransformInput, TransformOutput = TransformOutput, DataCaptureConfig = DataCaptureConfig, TransformResources = TransformResources, DataProcessing = DataProcessing, Tags = Tags, ExperimentConfig = ExperimentConfig)
  output <- .sagemaker$create_transform_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_transform_job <- sagemaker_create_transform_job

#' Creates an SageMaker trial
#'
#' @description
#' Creates an SageMaker *trial*. A trial is a set of steps called *trial components* that produce a machine learning model. A trial is part of a single SageMaker *experiment*.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_trial/](https://www.paws-r-sdk.com/docs/sagemaker_create_trial/) for full documentation.
#'
#' @param TrialName &#91;required&#93; The name of the trial. The name must be unique in your Amazon Web
#' Services account and is not case-sensitive.
#' @param DisplayName The name of the trial as displayed. The name doesn't need to be unique.
#' If `DisplayName` isn't specified, `TrialName` is displayed.
#' @param ExperimentName &#91;required&#93; The name of the experiment to associate the trial with.
#' @param MetadataProperties 
#' @param Tags A list of tags to associate with the trial. You can use
#' [`search`][sagemaker_search] API to search on the tags.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_trial
sagemaker_create_trial <- function(TrialName, DisplayName = NULL, ExperimentName, MetadataProperties = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateTrial",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_trial_input(TrialName = TrialName, DisplayName = DisplayName, ExperimentName = ExperimentName, MetadataProperties = MetadataProperties, Tags = Tags)
  output <- .sagemaker$create_trial_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_trial <- sagemaker_create_trial

#' Creates a trial component, which is a stage of a machine learning trial
#'
#' @description
#' Creates a *trial component*, which is a stage of a machine learning *trial*. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_trial_component/](https://www.paws-r-sdk.com/docs/sagemaker_create_trial_component/) for full documentation.
#'
#' @param TrialComponentName &#91;required&#93; The name of the component. The name must be unique in your Amazon Web
#' Services account and is not case-sensitive.
#' @param DisplayName The name of the component as displayed. The name doesn't need to be
#' unique. If `DisplayName` isn't specified, `TrialComponentName` is
#' displayed.
#' @param Status The status of the component. States include:
#' 
#' -   InProgress
#' 
#' -   Completed
#' 
#' -   Failed
#' @param StartTime When the component started.
#' @param EndTime When the component ended.
#' @param Parameters The hyperparameters for the component.
#' @param InputArtifacts The input artifacts for the component. Examples of input artifacts are
#' datasets, algorithms, hyperparameters, source code, and instance types.
#' @param OutputArtifacts The output artifacts for the component. Examples of output artifacts are
#' metrics, snapshots, logs, and images.
#' @param MetadataProperties 
#' @param Tags A list of tags to associate with the component. You can use
#' [`search`][sagemaker_search] API to search on the tags.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_trial_component
sagemaker_create_trial_component <- function(TrialComponentName, DisplayName = NULL, Status = NULL, StartTime = NULL, EndTime = NULL, Parameters = NULL, InputArtifacts = NULL, OutputArtifacts = NULL, MetadataProperties = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateTrialComponent",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_trial_component_input(TrialComponentName = TrialComponentName, DisplayName = DisplayName, Status = Status, StartTime = StartTime, EndTime = EndTime, Parameters = Parameters, InputArtifacts = InputArtifacts, OutputArtifacts = OutputArtifacts, MetadataProperties = MetadataProperties, Tags = Tags)
  output <- .sagemaker$create_trial_component_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_trial_component <- sagemaker_create_trial_component

#' Creates a user profile
#'
#' @description
#' Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_user_profile/](https://www.paws-r-sdk.com/docs/sagemaker_create_user_profile/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The ID of the associated Domain.
#' @param UserProfileName &#91;required&#93; A name for the UserProfile. This value is not case sensitive.
#' @param SingleSignOnUserIdentifier A specifier for the type of value specified in SingleSignOnUserValue.
#' Currently, the only supported value is "UserName". If the Domain's
#' AuthMode is IAM Identity Center, this field is required. If the Domain's
#' AuthMode is not IAM Identity Center, this field cannot be specified.
#' @param SingleSignOnUserValue The username of the associated Amazon Web Services Single Sign-On User
#' for this UserProfile. If the Domain's AuthMode is IAM Identity Center,
#' this field is required, and must match a valid username of a user in
#' your directory. If the Domain's AuthMode is not IAM Identity Center,
#' this field cannot be specified.
#' @param Tags Each tag consists of a key and an optional value. Tag keys must be
#' unique per resource.
#' 
#' Tags that you specify for the User Profile are also added to all Apps
#' that the User Profile launches.
#' @param UserSettings A collection of settings.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_user_profile
sagemaker_create_user_profile <- function(DomainId, UserProfileName, SingleSignOnUserIdentifier = NULL, SingleSignOnUserValue = NULL, Tags = NULL, UserSettings = NULL) {
  op <- new_operation(
    name = "CreateUserProfile",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_user_profile_input(DomainId = DomainId, UserProfileName = UserProfileName, SingleSignOnUserIdentifier = SingleSignOnUserIdentifier, SingleSignOnUserValue = SingleSignOnUserValue, Tags = Tags, UserSettings = UserSettings)
  output <- .sagemaker$create_user_profile_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_user_profile <- sagemaker_create_user_profile

#' Use this operation to create a workforce
#'
#' @description
#' Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_workforce/](https://www.paws-r-sdk.com/docs/sagemaker_create_workforce/) for full documentation.
#'
#' @param CognitoConfig Use this parameter to configure an Amazon Cognito private workforce. A
#' single Cognito workforce is created using and corresponds to a single
#' [Amazon Cognito user
#' pool](https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html).
#' 
#' Do not use `OidcConfig` if you specify values for `CognitoConfig`.
#' @param OidcConfig Use this parameter to configure a private workforce using your own OIDC
#' Identity Provider.
#' 
#' Do not use `CognitoConfig` if you specify values for `OidcConfig`.
#' @param SourceIpConfig 
#' @param WorkforceName &#91;required&#93; The name of the private workforce.
#' @param Tags An array of key-value pairs that contain metadata to help you categorize
#' and organize our workforce. Each tag consists of a key and a value, both
#' of which you define.
#' @param WorkforceVpcConfig Use this parameter to configure a workforce using VPC.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_workforce
sagemaker_create_workforce <- function(CognitoConfig = NULL, OidcConfig = NULL, SourceIpConfig = NULL, WorkforceName, Tags = NULL, WorkforceVpcConfig = NULL) {
  op <- new_operation(
    name = "CreateWorkforce",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_workforce_input(CognitoConfig = CognitoConfig, OidcConfig = OidcConfig, SourceIpConfig = SourceIpConfig, WorkforceName = WorkforceName, Tags = Tags, WorkforceVpcConfig = WorkforceVpcConfig)
  output <- .sagemaker$create_workforce_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_workforce <- sagemaker_create_workforce

#' Creates a new work team for labeling your data
#'
#' @description
#' Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_create_workteam/](https://www.paws-r-sdk.com/docs/sagemaker_create_workteam/) for full documentation.
#'
#' @param WorkteamName &#91;required&#93; The name of the work team. Use this name to identify the work team.
#' @param WorkforceName The name of the workforce.
#' @param MemberDefinitions &#91;required&#93; A list of `MemberDefinition` objects that contains objects that identify
#' the workers that make up the work team.
#' 
#' Workforces can be created using Amazon Cognito or your own OIDC Identity
#' Provider (IdP). For private workforces created using Amazon Cognito use
#' `CognitoMemberDefinition`. For workforces created using your own OIDC
#' identity provider (IdP) use `OidcMemberDefinition`. Do not provide input
#' for both of these parameters in a single request.
#' 
#' For workforces created using Amazon Cognito, private work teams
#' correspond to Amazon Cognito *user groups* within the user pool used to
#' create a workforce. All of the `CognitoMemberDefinition` objects that
#' make up the member definition must have the same `ClientId` and
#' `UserPool` values. To add a Amazon Cognito user group to an existing
#' worker pool, see Adding groups to a User Pool. For more information
#' about user pools, see [Amazon Cognito User
#' Pools](https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html).
#' 
#' For workforces created using your own OIDC IdP, specify the user groups
#' that you want to include in your private work team in
#' `OidcMemberDefinition` by listing those groups in `Groups`.
#' @param Description &#91;required&#93; A description of the work team.
#' @param NotificationConfiguration Configures notification of workers regarding available or expiring work
#' items.
#' @param Tags An array of key-value pairs.
#' 
#' For more information, see [Resource
#' Tag](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-resource-tags.html)
#' and [Using Cost Allocation
#' Tags](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what)
#' in the *Amazon Web Services Billing and Cost Management User Guide*.
#'
#' @keywords internal
#'
#' @rdname sagemaker_create_workteam
sagemaker_create_workteam <- function(WorkteamName, WorkforceName = NULL, MemberDefinitions, Description, NotificationConfiguration = NULL, Tags = NULL) {
  op <- new_operation(
    name = "CreateWorkteam",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$create_workteam_input(WorkteamName = WorkteamName, WorkforceName = WorkforceName, MemberDefinitions = MemberDefinitions, Description = Description, NotificationConfiguration = NotificationConfiguration, Tags = Tags)
  output <- .sagemaker$create_workteam_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$create_workteam <- sagemaker_create_workteam

#' Deletes an action
#'
#' @description
#' Deletes an action.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_action/](https://www.paws-r-sdk.com/docs/sagemaker_delete_action/) for full documentation.
#'
#' @param ActionName &#91;required&#93; The name of the action to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_action
sagemaker_delete_action <- function(ActionName) {
  op <- new_operation(
    name = "DeleteAction",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_action_input(ActionName = ActionName)
  output <- .sagemaker$delete_action_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_action <- sagemaker_delete_action

#' Removes the specified algorithm from your account
#'
#' @description
#' Removes the specified algorithm from your account.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_algorithm/](https://www.paws-r-sdk.com/docs/sagemaker_delete_algorithm/) for full documentation.
#'
#' @param AlgorithmName &#91;required&#93; The name of the algorithm to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_algorithm
sagemaker_delete_algorithm <- function(AlgorithmName) {
  op <- new_operation(
    name = "DeleteAlgorithm",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_algorithm_input(AlgorithmName = AlgorithmName)
  output <- .sagemaker$delete_algorithm_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_algorithm <- sagemaker_delete_algorithm

#' Used to stop and delete an app
#'
#' @description
#' Used to stop and delete an app.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_app/](https://www.paws-r-sdk.com/docs/sagemaker_delete_app/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param UserProfileName The user profile name. If this value is not set, then `SpaceName` must
#' be set.
#' @param AppType &#91;required&#93; The type of app.
#' @param AppName &#91;required&#93; The name of the app.
#' @param SpaceName The name of the space. If this value is not set, then `UserProfileName`
#' must be set.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_app
sagemaker_delete_app <- function(DomainId, UserProfileName = NULL, AppType, AppName, SpaceName = NULL) {
  op <- new_operation(
    name = "DeleteApp",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_app_input(DomainId = DomainId, UserProfileName = UserProfileName, AppType = AppType, AppName = AppName, SpaceName = SpaceName)
  output <- .sagemaker$delete_app_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_app <- sagemaker_delete_app

#' Deletes an AppImageConfig
#'
#' @description
#' Deletes an AppImageConfig.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_app_image_config/](https://www.paws-r-sdk.com/docs/sagemaker_delete_app_image_config/) for full documentation.
#'
#' @param AppImageConfigName &#91;required&#93; The name of the AppImageConfig to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_app_image_config
sagemaker_delete_app_image_config <- function(AppImageConfigName) {
  op <- new_operation(
    name = "DeleteAppImageConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_app_image_config_input(AppImageConfigName = AppImageConfigName)
  output <- .sagemaker$delete_app_image_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_app_image_config <- sagemaker_delete_app_image_config

#' Deletes an artifact
#'
#' @description
#' Deletes an artifact. Either `ArtifactArn` or `Source` must be specified.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_artifact/](https://www.paws-r-sdk.com/docs/sagemaker_delete_artifact/) for full documentation.
#'
#' @param ArtifactArn The Amazon Resource Name (ARN) of the artifact to delete.
#' @param Source The URI of the source.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_artifact
sagemaker_delete_artifact <- function(ArtifactArn = NULL, Source = NULL) {
  op <- new_operation(
    name = "DeleteArtifact",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_artifact_input(ArtifactArn = ArtifactArn, Source = Source)
  output <- .sagemaker$delete_artifact_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_artifact <- sagemaker_delete_artifact

#' Deletes an association
#'
#' @description
#' Deletes an association.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_association/](https://www.paws-r-sdk.com/docs/sagemaker_delete_association/) for full documentation.
#'
#' @param SourceArn &#91;required&#93; The ARN of the source.
#' @param DestinationArn &#91;required&#93; The Amazon Resource Name (ARN) of the destination.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_association
sagemaker_delete_association <- function(SourceArn, DestinationArn) {
  op <- new_operation(
    name = "DeleteAssociation",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_association_input(SourceArn = SourceArn, DestinationArn = DestinationArn)
  output <- .sagemaker$delete_association_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_association <- sagemaker_delete_association

#' Deletes the specified Git repository from your account
#'
#' @description
#' Deletes the specified Git repository from your account.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_code_repository/](https://www.paws-r-sdk.com/docs/sagemaker_delete_code_repository/) for full documentation.
#'
#' @param CodeRepositoryName &#91;required&#93; The name of the Git repository to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_code_repository
sagemaker_delete_code_repository <- function(CodeRepositoryName) {
  op <- new_operation(
    name = "DeleteCodeRepository",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_code_repository_input(CodeRepositoryName = CodeRepositoryName)
  output <- .sagemaker$delete_code_repository_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_code_repository <- sagemaker_delete_code_repository

#' Deletes an context
#'
#' @description
#' Deletes an context.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_context/](https://www.paws-r-sdk.com/docs/sagemaker_delete_context/) for full documentation.
#'
#' @param ContextName &#91;required&#93; The name of the context to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_context
sagemaker_delete_context <- function(ContextName) {
  op <- new_operation(
    name = "DeleteContext",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_context_input(ContextName = ContextName)
  output <- .sagemaker$delete_context_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_context <- sagemaker_delete_context

#' Deletes a data quality monitoring job definition
#'
#' @description
#' Deletes a data quality monitoring job definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_data_quality_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_delete_data_quality_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the data quality monitoring job definition to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_data_quality_job_definition
sagemaker_delete_data_quality_job_definition <- function(JobDefinitionName) {
  op <- new_operation(
    name = "DeleteDataQualityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_data_quality_job_definition_input(JobDefinitionName = JobDefinitionName)
  output <- .sagemaker$delete_data_quality_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_data_quality_job_definition <- sagemaker_delete_data_quality_job_definition

#' Deletes a fleet
#'
#' @description
#' Deletes a fleet.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_device_fleet/](https://www.paws-r-sdk.com/docs/sagemaker_delete_device_fleet/) for full documentation.
#'
#' @param DeviceFleetName &#91;required&#93; The name of the fleet to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_device_fleet
sagemaker_delete_device_fleet <- function(DeviceFleetName) {
  op <- new_operation(
    name = "DeleteDeviceFleet",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_device_fleet_input(DeviceFleetName = DeviceFleetName)
  output <- .sagemaker$delete_device_fleet_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_device_fleet <- sagemaker_delete_device_fleet

#' Used to delete a domain
#'
#' @description
#' Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_domain/](https://www.paws-r-sdk.com/docs/sagemaker_delete_domain/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param RetentionPolicy The retention policy for this domain, which specifies whether resources
#' will be retained after the Domain is deleted. By default, all resources
#' are retained (not automatically deleted).
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_domain
sagemaker_delete_domain <- function(DomainId, RetentionPolicy = NULL) {
  op <- new_operation(
    name = "DeleteDomain",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_domain_input(DomainId = DomainId, RetentionPolicy = RetentionPolicy)
  output <- .sagemaker$delete_domain_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_domain <- sagemaker_delete_domain

#' Deletes an edge deployment plan if (and only if) all the stages in the
#' plan are inactive or there are no stages in the plan
#'
#' @description
#' Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_edge_deployment_plan/](https://www.paws-r-sdk.com/docs/sagemaker_delete_edge_deployment_plan/) for full documentation.
#'
#' @param EdgeDeploymentPlanName &#91;required&#93; The name of the edge deployment plan to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_edge_deployment_plan
sagemaker_delete_edge_deployment_plan <- function(EdgeDeploymentPlanName) {
  op <- new_operation(
    name = "DeleteEdgeDeploymentPlan",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_edge_deployment_plan_input(EdgeDeploymentPlanName = EdgeDeploymentPlanName)
  output <- .sagemaker$delete_edge_deployment_plan_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_edge_deployment_plan <- sagemaker_delete_edge_deployment_plan

#' Delete a stage in an edge deployment plan if (and only if) the stage is
#' inactive
#'
#' @description
#' Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_edge_deployment_stage/](https://www.paws-r-sdk.com/docs/sagemaker_delete_edge_deployment_stage/) for full documentation.
#'
#' @param EdgeDeploymentPlanName &#91;required&#93; The name of the edge deployment plan from which the stage will be
#' deleted.
#' @param StageName &#91;required&#93; The name of the stage.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_edge_deployment_stage
sagemaker_delete_edge_deployment_stage <- function(EdgeDeploymentPlanName, StageName) {
  op <- new_operation(
    name = "DeleteEdgeDeploymentStage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_edge_deployment_stage_input(EdgeDeploymentPlanName = EdgeDeploymentPlanName, StageName = StageName)
  output <- .sagemaker$delete_edge_deployment_stage_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_edge_deployment_stage <- sagemaker_delete_edge_deployment_stage

#' Deletes an endpoint
#'
#' @description
#' Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_endpoint/](https://www.paws-r-sdk.com/docs/sagemaker_delete_endpoint/) for full documentation.
#'
#' @param EndpointName &#91;required&#93; The name of the endpoint that you want to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_endpoint
sagemaker_delete_endpoint <- function(EndpointName) {
  op <- new_operation(
    name = "DeleteEndpoint",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_endpoint_input(EndpointName = EndpointName)
  output <- .sagemaker$delete_endpoint_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_endpoint <- sagemaker_delete_endpoint

#' Deletes an endpoint configuration
#'
#' @description
#' Deletes an endpoint configuration. The [`delete_endpoint_config`][sagemaker_delete_endpoint_config] API deletes only the specified configuration. It does not delete endpoints created using the configuration.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_endpoint_config/](https://www.paws-r-sdk.com/docs/sagemaker_delete_endpoint_config/) for full documentation.
#'
#' @param EndpointConfigName &#91;required&#93; The name of the endpoint configuration that you want to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_endpoint_config
sagemaker_delete_endpoint_config <- function(EndpointConfigName) {
  op <- new_operation(
    name = "DeleteEndpointConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_endpoint_config_input(EndpointConfigName = EndpointConfigName)
  output <- .sagemaker$delete_endpoint_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_endpoint_config <- sagemaker_delete_endpoint_config

#' Deletes an SageMaker experiment
#'
#' @description
#' Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the [`list_trials`][sagemaker_list_trials] API to get a list of the trials associated with the experiment.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_experiment/](https://www.paws-r-sdk.com/docs/sagemaker_delete_experiment/) for full documentation.
#'
#' @param ExperimentName &#91;required&#93; The name of the experiment to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_experiment
sagemaker_delete_experiment <- function(ExperimentName) {
  op <- new_operation(
    name = "DeleteExperiment",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_experiment_input(ExperimentName = ExperimentName)
  output <- .sagemaker$delete_experiment_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_experiment <- sagemaker_delete_experiment

#' Delete the FeatureGroup and any data that was written to the OnlineStore
#' of the FeatureGroup
#'
#' @description
#' Delete the `FeatureGroup` and any data that was written to the `OnlineStore` of the `FeatureGroup`. Data cannot be accessed from the `OnlineStore` immediately after [`delete_feature_group`][sagemaker_delete_feature_group] is called.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_feature_group/](https://www.paws-r-sdk.com/docs/sagemaker_delete_feature_group/) for full documentation.
#'
#' @param FeatureGroupName &#91;required&#93; The name of the `FeatureGroup` you want to delete. The name must be
#' unique within an Amazon Web Services Region in an Amazon Web Services
#' account.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_feature_group
sagemaker_delete_feature_group <- function(FeatureGroupName) {
  op <- new_operation(
    name = "DeleteFeatureGroup",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_feature_group_input(FeatureGroupName = FeatureGroupName)
  output <- .sagemaker$delete_feature_group_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_feature_group <- sagemaker_delete_feature_group

#' Deletes the specified flow definition
#'
#' @description
#' Deletes the specified flow definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_flow_definition/](https://www.paws-r-sdk.com/docs/sagemaker_delete_flow_definition/) for full documentation.
#'
#' @param FlowDefinitionName &#91;required&#93; The name of the flow definition you are deleting.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_flow_definition
sagemaker_delete_flow_definition <- function(FlowDefinitionName) {
  op <- new_operation(
    name = "DeleteFlowDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_flow_definition_input(FlowDefinitionName = FlowDefinitionName)
  output <- .sagemaker$delete_flow_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_flow_definition <- sagemaker_delete_flow_definition

#' Delete a hub
#'
#' @description
#' Delete a hub.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_hub/](https://www.paws-r-sdk.com/docs/sagemaker_delete_hub/) for full documentation.
#'
#' @param HubName &#91;required&#93; The name of the hub to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_hub
sagemaker_delete_hub <- function(HubName) {
  op <- new_operation(
    name = "DeleteHub",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_hub_input(HubName = HubName)
  output <- .sagemaker$delete_hub_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_hub <- sagemaker_delete_hub

#' Delete the contents of a hub
#'
#' @description
#' Delete the contents of a hub.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_hub_content/](https://www.paws-r-sdk.com/docs/sagemaker_delete_hub_content/) for full documentation.
#'
#' @param HubName &#91;required&#93; The name of the hub that you want to delete content in.
#' @param HubContentType &#91;required&#93; The type of content that you want to delete from a hub.
#' @param HubContentName &#91;required&#93; The name of the content that you want to delete from a hub.
#' @param HubContentVersion &#91;required&#93; The version of the content that you want to delete from a hub.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_hub_content
sagemaker_delete_hub_content <- function(HubName, HubContentType, HubContentName, HubContentVersion) {
  op <- new_operation(
    name = "DeleteHubContent",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_hub_content_input(HubName = HubName, HubContentType = HubContentType, HubContentName = HubContentName, HubContentVersion = HubContentVersion)
  output <- .sagemaker$delete_hub_content_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_hub_content <- sagemaker_delete_hub_content

#' Use this operation to delete a human task user interface (worker task
#' template)
#'
#' @description
#' Use this operation to delete a human task user interface (worker task template).
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_human_task_ui/](https://www.paws-r-sdk.com/docs/sagemaker_delete_human_task_ui/) for full documentation.
#'
#' @param HumanTaskUiName &#91;required&#93; The name of the human task user interface (work task template) you want
#' to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_human_task_ui
sagemaker_delete_human_task_ui <- function(HumanTaskUiName) {
  op <- new_operation(
    name = "DeleteHumanTaskUi",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_human_task_ui_input(HumanTaskUiName = HumanTaskUiName)
  output <- .sagemaker$delete_human_task_ui_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_human_task_ui <- sagemaker_delete_human_task_ui

#' Deletes a SageMaker image and all versions of the image
#'
#' @description
#' Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_image/](https://www.paws-r-sdk.com/docs/sagemaker_delete_image/) for full documentation.
#'
#' @param ImageName &#91;required&#93; The name of the image to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_image
sagemaker_delete_image <- function(ImageName) {
  op <- new_operation(
    name = "DeleteImage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_image_input(ImageName = ImageName)
  output <- .sagemaker$delete_image_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_image <- sagemaker_delete_image

#' Deletes a version of a SageMaker image
#'
#' @description
#' Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_image_version/](https://www.paws-r-sdk.com/docs/sagemaker_delete_image_version/) for full documentation.
#'
#' @param ImageName &#91;required&#93; The name of the image to delete.
#' @param Version The version to delete.
#' @param Alias The alias of the image to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_image_version
sagemaker_delete_image_version <- function(ImageName, Version = NULL, Alias = NULL) {
  op <- new_operation(
    name = "DeleteImageVersion",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_image_version_input(ImageName = ImageName, Version = Version, Alias = Alias)
  output <- .sagemaker$delete_image_version_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_image_version <- sagemaker_delete_image_version

#' Deletes an inference experiment
#'
#' @description
#' Deletes an inference experiment.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_inference_experiment/](https://www.paws-r-sdk.com/docs/sagemaker_delete_inference_experiment/) for full documentation.
#'
#' @param Name &#91;required&#93; The name of the inference experiment you want to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_inference_experiment
sagemaker_delete_inference_experiment <- function(Name) {
  op <- new_operation(
    name = "DeleteInferenceExperiment",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_inference_experiment_input(Name = Name)
  output <- .sagemaker$delete_inference_experiment_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_inference_experiment <- sagemaker_delete_inference_experiment

#' Deletes a model
#'
#' @description
#' Deletes a model. The [`delete_model`][sagemaker_delete_model] API deletes only the model entry that was created in SageMaker when you called the [`create_model`][sagemaker_create_model] API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model/) for full documentation.
#'
#' @param ModelName &#91;required&#93; The name of the model to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model
sagemaker_delete_model <- function(ModelName) {
  op <- new_operation(
    name = "DeleteModel",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_input(ModelName = ModelName)
  output <- .sagemaker$delete_model_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model <- sagemaker_delete_model

#' Deletes an Amazon SageMaker model bias job definition
#'
#' @description
#' Deletes an Amazon SageMaker model bias job definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_bias_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_bias_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the model bias job definition to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_bias_job_definition
sagemaker_delete_model_bias_job_definition <- function(JobDefinitionName) {
  op <- new_operation(
    name = "DeleteModelBiasJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_bias_job_definition_input(JobDefinitionName = JobDefinitionName)
  output <- .sagemaker$delete_model_bias_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_bias_job_definition <- sagemaker_delete_model_bias_job_definition

#' Deletes an Amazon SageMaker Model Card
#'
#' @description
#' Deletes an Amazon SageMaker Model Card.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_card/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_card/) for full documentation.
#'
#' @param ModelCardName &#91;required&#93; The name of the model card to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_card
sagemaker_delete_model_card <- function(ModelCardName) {
  op <- new_operation(
    name = "DeleteModelCard",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_card_input(ModelCardName = ModelCardName)
  output <- .sagemaker$delete_model_card_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_card <- sagemaker_delete_model_card

#' Deletes an Amazon SageMaker model explainability job definition
#'
#' @description
#' Deletes an Amazon SageMaker model explainability job definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_explainability_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_explainability_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the model explainability job definition to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_explainability_job_definition
sagemaker_delete_model_explainability_job_definition <- function(JobDefinitionName) {
  op <- new_operation(
    name = "DeleteModelExplainabilityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_explainability_job_definition_input(JobDefinitionName = JobDefinitionName)
  output <- .sagemaker$delete_model_explainability_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_explainability_job_definition <- sagemaker_delete_model_explainability_job_definition

#' Deletes a model package
#'
#' @description
#' Deletes a model package.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package/) for full documentation.
#'
#' @param ModelPackageName &#91;required&#93; The name or Amazon Resource Name (ARN) of the model package to delete.
#' 
#' When you specify a name, the name must have 1 to 63 characters. Valid
#' characters are a-z, A-Z, 0-9, and - (hyphen).
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_package
sagemaker_delete_model_package <- function(ModelPackageName) {
  op <- new_operation(
    name = "DeleteModelPackage",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_package_input(ModelPackageName = ModelPackageName)
  output <- .sagemaker$delete_model_package_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_package <- sagemaker_delete_model_package

#' Deletes the specified model group
#'
#' @description
#' Deletes the specified model group.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package_group/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package_group/) for full documentation.
#'
#' @param ModelPackageGroupName &#91;required&#93; The name of the model group to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_package_group
sagemaker_delete_model_package_group <- function(ModelPackageGroupName) {
  op <- new_operation(
    name = "DeleteModelPackageGroup",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_package_group_input(ModelPackageGroupName = ModelPackageGroupName)
  output <- .sagemaker$delete_model_package_group_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_package_group <- sagemaker_delete_model_package_group

#' Deletes a model group resource policy
#'
#' @description
#' Deletes a model group resource policy.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package_group_policy/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_package_group_policy/) for full documentation.
#'
#' @param ModelPackageGroupName &#91;required&#93; The name of the model group for which to delete the policy.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_package_group_policy
sagemaker_delete_model_package_group_policy <- function(ModelPackageGroupName) {
  op <- new_operation(
    name = "DeleteModelPackageGroupPolicy",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_package_group_policy_input(ModelPackageGroupName = ModelPackageGroupName)
  output <- .sagemaker$delete_model_package_group_policy_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_package_group_policy <- sagemaker_delete_model_package_group_policy

#' Deletes the secified model quality monitoring job definition
#'
#' @description
#' Deletes the secified model quality monitoring job definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_model_quality_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_delete_model_quality_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the model quality monitoring job definition to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_model_quality_job_definition
sagemaker_delete_model_quality_job_definition <- function(JobDefinitionName) {
  op <- new_operation(
    name = "DeleteModelQualityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_model_quality_job_definition_input(JobDefinitionName = JobDefinitionName)
  output <- .sagemaker$delete_model_quality_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_model_quality_job_definition <- sagemaker_delete_model_quality_job_definition

#' Deletes a monitoring schedule
#'
#' @description
#' Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_monitoring_schedule/](https://www.paws-r-sdk.com/docs/sagemaker_delete_monitoring_schedule/) for full documentation.
#'
#' @param MonitoringScheduleName &#91;required&#93; The name of the monitoring schedule to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_monitoring_schedule
sagemaker_delete_monitoring_schedule <- function(MonitoringScheduleName) {
  op <- new_operation(
    name = "DeleteMonitoringSchedule",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_monitoring_schedule_input(MonitoringScheduleName = MonitoringScheduleName)
  output <- .sagemaker$delete_monitoring_schedule_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_monitoring_schedule <- sagemaker_delete_monitoring_schedule

#' Deletes an SageMaker notebook instance
#'
#' @description
#' Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the [`stop_notebook_instance`][sagemaker_stop_notebook_instance] API.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_notebook_instance/](https://www.paws-r-sdk.com/docs/sagemaker_delete_notebook_instance/) for full documentation.
#'
#' @param NotebookInstanceName &#91;required&#93; The name of the SageMaker notebook instance to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_notebook_instance
sagemaker_delete_notebook_instance <- function(NotebookInstanceName) {
  op <- new_operation(
    name = "DeleteNotebookInstance",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_notebook_instance_input(NotebookInstanceName = NotebookInstanceName)
  output <- .sagemaker$delete_notebook_instance_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_notebook_instance <- sagemaker_delete_notebook_instance

#' Deletes a notebook instance lifecycle configuration
#'
#' @description
#' Deletes a notebook instance lifecycle configuration.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_notebook_instance_lifecycle_config/](https://www.paws-r-sdk.com/docs/sagemaker_delete_notebook_instance_lifecycle_config/) for full documentation.
#'
#' @param NotebookInstanceLifecycleConfigName &#91;required&#93; The name of the lifecycle configuration to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_notebook_instance_lifecycle_config
sagemaker_delete_notebook_instance_lifecycle_config <- function(NotebookInstanceLifecycleConfigName) {
  op <- new_operation(
    name = "DeleteNotebookInstanceLifecycleConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_notebook_instance_lifecycle_config_input(NotebookInstanceLifecycleConfigName = NotebookInstanceLifecycleConfigName)
  output <- .sagemaker$delete_notebook_instance_lifecycle_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_notebook_instance_lifecycle_config <- sagemaker_delete_notebook_instance_lifecycle_config

#' Deletes a pipeline if there are no running instances of the pipeline
#'
#' @description
#' Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the [`stop_pipeline_execution`][sagemaker_stop_pipeline_execution] API. When you delete a pipeline, all instances of the pipeline are deleted.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_pipeline/](https://www.paws-r-sdk.com/docs/sagemaker_delete_pipeline/) for full documentation.
#'
#' @param PipelineName &#91;required&#93; The name of the pipeline to delete.
#' @param ClientRequestToken &#91;required&#93; A unique, case-sensitive identifier that you provide to ensure the
#' idempotency of the operation. An idempotent operation completes no more
#' than one time.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_pipeline
sagemaker_delete_pipeline <- function(PipelineName, ClientRequestToken) {
  op <- new_operation(
    name = "DeletePipeline",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_pipeline_input(PipelineName = PipelineName, ClientRequestToken = ClientRequestToken)
  output <- .sagemaker$delete_pipeline_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_pipeline <- sagemaker_delete_pipeline

#' Delete the specified project
#'
#' @description
#' Delete the specified project.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_project/](https://www.paws-r-sdk.com/docs/sagemaker_delete_project/) for full documentation.
#'
#' @param ProjectName &#91;required&#93; The name of the project to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_project
sagemaker_delete_project <- function(ProjectName) {
  op <- new_operation(
    name = "DeleteProject",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_project_input(ProjectName = ProjectName)
  output <- .sagemaker$delete_project_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_project <- sagemaker_delete_project

#' Used to delete a space
#'
#' @description
#' Used to delete a space.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_space/](https://www.paws-r-sdk.com/docs/sagemaker_delete_space/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The ID of the associated Domain.
#' @param SpaceName &#91;required&#93; The name of the space.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_space
sagemaker_delete_space <- function(DomainId, SpaceName) {
  op <- new_operation(
    name = "DeleteSpace",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_space_input(DomainId = DomainId, SpaceName = SpaceName)
  output <- .sagemaker$delete_space_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_space <- sagemaker_delete_space

#' Deletes the Studio Lifecycle Configuration
#'
#' @description
#' Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_studio_lifecycle_config/](https://www.paws-r-sdk.com/docs/sagemaker_delete_studio_lifecycle_config/) for full documentation.
#'
#' @param StudioLifecycleConfigName &#91;required&#93; The name of the Studio Lifecycle Configuration to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_studio_lifecycle_config
sagemaker_delete_studio_lifecycle_config <- function(StudioLifecycleConfigName) {
  op <- new_operation(
    name = "DeleteStudioLifecycleConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_studio_lifecycle_config_input(StudioLifecycleConfigName = StudioLifecycleConfigName)
  output <- .sagemaker$delete_studio_lifecycle_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_studio_lifecycle_config <- sagemaker_delete_studio_lifecycle_config

#' Deletes the specified tags from an SageMaker resource
#'
#' @description
#' Deletes the specified tags from an SageMaker resource.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_tags/](https://www.paws-r-sdk.com/docs/sagemaker_delete_tags/) for full documentation.
#'
#' @param ResourceArn &#91;required&#93; The Amazon Resource Name (ARN) of the resource whose tags you want to
#' delete.
#' @param TagKeys &#91;required&#93; An array or one or more tag keys to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_tags
sagemaker_delete_tags <- function(ResourceArn, TagKeys) {
  op <- new_operation(
    name = "DeleteTags",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_tags_input(ResourceArn = ResourceArn, TagKeys = TagKeys)
  output <- .sagemaker$delete_tags_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_tags <- sagemaker_delete_tags

#' Deletes the specified trial
#'
#' @description
#' Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the [`describe_trial_component`][sagemaker_describe_trial_component] API to get the list of trial components.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_trial/](https://www.paws-r-sdk.com/docs/sagemaker_delete_trial/) for full documentation.
#'
#' @param TrialName &#91;required&#93; The name of the trial to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_trial
sagemaker_delete_trial <- function(TrialName) {
  op <- new_operation(
    name = "DeleteTrial",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_trial_input(TrialName = TrialName)
  output <- .sagemaker$delete_trial_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_trial <- sagemaker_delete_trial

#' Deletes the specified trial component
#'
#' @description
#' Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the [`disassociate_trial_component`][sagemaker_disassociate_trial_component] API.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_trial_component/](https://www.paws-r-sdk.com/docs/sagemaker_delete_trial_component/) for full documentation.
#'
#' @param TrialComponentName &#91;required&#93; The name of the component to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_trial_component
sagemaker_delete_trial_component <- function(TrialComponentName) {
  op <- new_operation(
    name = "DeleteTrialComponent",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_trial_component_input(TrialComponentName = TrialComponentName)
  output <- .sagemaker$delete_trial_component_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_trial_component <- sagemaker_delete_trial_component

#' Deletes a user profile
#'
#' @description
#' Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_user_profile/](https://www.paws-r-sdk.com/docs/sagemaker_delete_user_profile/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param UserProfileName &#91;required&#93; The user profile name.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_user_profile
sagemaker_delete_user_profile <- function(DomainId, UserProfileName) {
  op <- new_operation(
    name = "DeleteUserProfile",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_user_profile_input(DomainId = DomainId, UserProfileName = UserProfileName)
  output <- .sagemaker$delete_user_profile_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_user_profile <- sagemaker_delete_user_profile

#' Use this operation to delete a workforce
#'
#' @description
#' Use this operation to delete a workforce.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_workforce/](https://www.paws-r-sdk.com/docs/sagemaker_delete_workforce/) for full documentation.
#'
#' @param WorkforceName &#91;required&#93; The name of the workforce.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_workforce
sagemaker_delete_workforce <- function(WorkforceName) {
  op <- new_operation(
    name = "DeleteWorkforce",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_workforce_input(WorkforceName = WorkforceName)
  output <- .sagemaker$delete_workforce_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_workforce <- sagemaker_delete_workforce

#' Deletes an existing work team
#'
#' @description
#' Deletes an existing work team. This operation can't be undone.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_delete_workteam/](https://www.paws-r-sdk.com/docs/sagemaker_delete_workteam/) for full documentation.
#'
#' @param WorkteamName &#91;required&#93; The name of the work team to delete.
#'
#' @keywords internal
#'
#' @rdname sagemaker_delete_workteam
sagemaker_delete_workteam <- function(WorkteamName) {
  op <- new_operation(
    name = "DeleteWorkteam",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$delete_workteam_input(WorkteamName = WorkteamName)
  output <- .sagemaker$delete_workteam_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$delete_workteam <- sagemaker_delete_workteam

#' Deregisters the specified devices
#'
#' @description
#' Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_deregister_devices/](https://www.paws-r-sdk.com/docs/sagemaker_deregister_devices/) for full documentation.
#'
#' @param DeviceFleetName &#91;required&#93; The name of the fleet the devices belong to.
#' @param DeviceNames &#91;required&#93; The unique IDs of the devices.
#'
#' @keywords internal
#'
#' @rdname sagemaker_deregister_devices
sagemaker_deregister_devices <- function(DeviceFleetName, DeviceNames) {
  op <- new_operation(
    name = "DeregisterDevices",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$deregister_devices_input(DeviceFleetName = DeviceFleetName, DeviceNames = DeviceNames)
  output <- .sagemaker$deregister_devices_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$deregister_devices <- sagemaker_deregister_devices

#' Describes an action
#'
#' @description
#' Describes an action.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_action/](https://www.paws-r-sdk.com/docs/sagemaker_describe_action/) for full documentation.
#'
#' @param ActionName &#91;required&#93; The name of the action to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_action
sagemaker_describe_action <- function(ActionName) {
  op <- new_operation(
    name = "DescribeAction",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_action_input(ActionName = ActionName)
  output <- .sagemaker$describe_action_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_action <- sagemaker_describe_action

#' Returns a description of the specified algorithm that is in your account
#'
#' @description
#' Returns a description of the specified algorithm that is in your account.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_algorithm/](https://www.paws-r-sdk.com/docs/sagemaker_describe_algorithm/) for full documentation.
#'
#' @param AlgorithmName &#91;required&#93; The name of the algorithm to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_algorithm
sagemaker_describe_algorithm <- function(AlgorithmName) {
  op <- new_operation(
    name = "DescribeAlgorithm",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_algorithm_input(AlgorithmName = AlgorithmName)
  output <- .sagemaker$describe_algorithm_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_algorithm <- sagemaker_describe_algorithm

#' Describes the app
#'
#' @description
#' Describes the app.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_app/](https://www.paws-r-sdk.com/docs/sagemaker_describe_app/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#' @param UserProfileName The user profile name. If this value is not set, then `SpaceName` must
#' be set.
#' @param AppType &#91;required&#93; The type of app.
#' @param AppName &#91;required&#93; The name of the app.
#' @param SpaceName The name of the space.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_app
sagemaker_describe_app <- function(DomainId, UserProfileName = NULL, AppType, AppName, SpaceName = NULL) {
  op <- new_operation(
    name = "DescribeApp",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_app_input(DomainId = DomainId, UserProfileName = UserProfileName, AppType = AppType, AppName = AppName, SpaceName = SpaceName)
  output <- .sagemaker$describe_app_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_app <- sagemaker_describe_app

#' Describes an AppImageConfig
#'
#' @description
#' Describes an AppImageConfig.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_app_image_config/](https://www.paws-r-sdk.com/docs/sagemaker_describe_app_image_config/) for full documentation.
#'
#' @param AppImageConfigName &#91;required&#93; The name of the AppImageConfig to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_app_image_config
sagemaker_describe_app_image_config <- function(AppImageConfigName) {
  op <- new_operation(
    name = "DescribeAppImageConfig",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_app_image_config_input(AppImageConfigName = AppImageConfigName)
  output <- .sagemaker$describe_app_image_config_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_app_image_config <- sagemaker_describe_app_image_config

#' Describes an artifact
#'
#' @description
#' Describes an artifact.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_artifact/](https://www.paws-r-sdk.com/docs/sagemaker_describe_artifact/) for full documentation.
#'
#' @param ArtifactArn &#91;required&#93; The Amazon Resource Name (ARN) of the artifact to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_artifact
sagemaker_describe_artifact <- function(ArtifactArn) {
  op <- new_operation(
    name = "DescribeArtifact",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_artifact_input(ArtifactArn = ArtifactArn)
  output <- .sagemaker$describe_artifact_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_artifact <- sagemaker_describe_artifact

#' Returns information about an AutoML job created by calling
#' CreateAutoMLJob
#'
#' @description
#' Returns information about an AutoML job created by calling [`create_auto_ml_job`][sagemaker_create_auto_ml_job].
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_auto_ml_job/](https://www.paws-r-sdk.com/docs/sagemaker_describe_auto_ml_job/) for full documentation.
#'
#' @param AutoMLJobName &#91;required&#93; Requests information about an AutoML job using its unique name.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_auto_ml_job
sagemaker_describe_auto_ml_job <- function(AutoMLJobName) {
  op <- new_operation(
    name = "DescribeAutoMLJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_auto_ml_job_input(AutoMLJobName = AutoMLJobName)
  output <- .sagemaker$describe_auto_ml_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_auto_ml_job <- sagemaker_describe_auto_ml_job

#' Returns information about an AutoML job created by calling
#' CreateAutoMLJobV2 or CreateAutoMLJob
#'
#' @description
#' Returns information about an AutoML job created by calling [`create_auto_ml_job_v2`][sagemaker_create_auto_ml_job_v2] or [`create_auto_ml_job`][sagemaker_create_auto_ml_job].
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_auto_ml_job_v2/](https://www.paws-r-sdk.com/docs/sagemaker_describe_auto_ml_job_v2/) for full documentation.
#'
#' @param AutoMLJobName &#91;required&#93; Requests information about an AutoML job V2 using its unique name.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_auto_ml_job_v2
sagemaker_describe_auto_ml_job_v2 <- function(AutoMLJobName) {
  op <- new_operation(
    name = "DescribeAutoMLJobV2",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_auto_ml_job_v2_input(AutoMLJobName = AutoMLJobName)
  output <- .sagemaker$describe_auto_ml_job_v2_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_auto_ml_job_v2 <- sagemaker_describe_auto_ml_job_v2

#' Gets details about the specified Git repository
#'
#' @description
#' Gets details about the specified Git repository.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_code_repository/](https://www.paws-r-sdk.com/docs/sagemaker_describe_code_repository/) for full documentation.
#'
#' @param CodeRepositoryName &#91;required&#93; The name of the Git repository to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_code_repository
sagemaker_describe_code_repository <- function(CodeRepositoryName) {
  op <- new_operation(
    name = "DescribeCodeRepository",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_code_repository_input(CodeRepositoryName = CodeRepositoryName)
  output <- .sagemaker$describe_code_repository_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_code_repository <- sagemaker_describe_code_repository

#' Returns information about a model compilation job
#'
#' @description
#' Returns information about a model compilation job.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_compilation_job/](https://www.paws-r-sdk.com/docs/sagemaker_describe_compilation_job/) for full documentation.
#'
#' @param CompilationJobName &#91;required&#93; The name of the model compilation job that you want information about.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_compilation_job
sagemaker_describe_compilation_job <- function(CompilationJobName) {
  op <- new_operation(
    name = "DescribeCompilationJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_compilation_job_input(CompilationJobName = CompilationJobName)
  output <- .sagemaker$describe_compilation_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_compilation_job <- sagemaker_describe_compilation_job

#' Describes a context
#'
#' @description
#' Describes a context.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_context/](https://www.paws-r-sdk.com/docs/sagemaker_describe_context/) for full documentation.
#'
#' @param ContextName &#91;required&#93; The name of the context to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_context
sagemaker_describe_context <- function(ContextName) {
  op <- new_operation(
    name = "DescribeContext",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_context_input(ContextName = ContextName)
  output <- .sagemaker$describe_context_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_context <- sagemaker_describe_context

#' Gets the details of a data quality monitoring job definition
#'
#' @description
#' Gets the details of a data quality monitoring job definition.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_data_quality_job_definition/](https://www.paws-r-sdk.com/docs/sagemaker_describe_data_quality_job_definition/) for full documentation.
#'
#' @param JobDefinitionName &#91;required&#93; The name of the data quality monitoring job definition to describe.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_data_quality_job_definition
sagemaker_describe_data_quality_job_definition <- function(JobDefinitionName) {
  op <- new_operation(
    name = "DescribeDataQualityJobDefinition",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_data_quality_job_definition_input(JobDefinitionName = JobDefinitionName)
  output <- .sagemaker$describe_data_quality_job_definition_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_data_quality_job_definition <- sagemaker_describe_data_quality_job_definition

#' Describes the device
#'
#' @description
#' Describes the device.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_device/](https://www.paws-r-sdk.com/docs/sagemaker_describe_device/) for full documentation.
#'
#' @param NextToken Next token of device description.
#' @param DeviceName &#91;required&#93; The unique ID of the device.
#' @param DeviceFleetName &#91;required&#93; The name of the fleet the devices belong to.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_device
sagemaker_describe_device <- function(NextToken = NULL, DeviceName, DeviceFleetName) {
  op <- new_operation(
    name = "DescribeDevice",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_device_input(NextToken = NextToken, DeviceName = DeviceName, DeviceFleetName = DeviceFleetName)
  output <- .sagemaker$describe_device_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_device <- sagemaker_describe_device

#' A description of the fleet the device belongs to
#'
#' @description
#' A description of the fleet the device belongs to.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_device_fleet/](https://www.paws-r-sdk.com/docs/sagemaker_describe_device_fleet/) for full documentation.
#'
#' @param DeviceFleetName &#91;required&#93; The name of the fleet.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_device_fleet
sagemaker_describe_device_fleet <- function(DeviceFleetName) {
  op <- new_operation(
    name = "DescribeDeviceFleet",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_device_fleet_input(DeviceFleetName = DeviceFleetName)
  output <- .sagemaker$describe_device_fleet_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_device_fleet <- sagemaker_describe_device_fleet

#' The description of the domain
#'
#' @description
#' The description of the domain.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_domain/](https://www.paws-r-sdk.com/docs/sagemaker_describe_domain/) for full documentation.
#'
#' @param DomainId &#91;required&#93; The domain ID.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_domain
sagemaker_describe_domain <- function(DomainId) {
  op <- new_operation(
    name = "DescribeDomain",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_domain_input(DomainId = DomainId)
  output <- .sagemaker$describe_domain_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_domain <- sagemaker_describe_domain

#' Describes an edge deployment plan with deployment status per stage
#'
#' @description
#' Describes an edge deployment plan with deployment status per stage.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_edge_deployment_plan/](https://www.paws-r-sdk.com/docs/sagemaker_describe_edge_deployment_plan/) for full documentation.
#'
#' @param EdgeDeploymentPlanName &#91;required&#93; The name of the deployment plan to describe.
#' @param NextToken If the edge deployment plan has enough stages to require tokening, then
#' this is the response from the last list of stages returned.
#' @param MaxResults The maximum number of results to select (50 by default).
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_edge_deployment_plan
sagemaker_describe_edge_deployment_plan <- function(EdgeDeploymentPlanName, NextToken = NULL, MaxResults = NULL) {
  op <- new_operation(
    name = "DescribeEdgeDeploymentPlan",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_edge_deployment_plan_input(EdgeDeploymentPlanName = EdgeDeploymentPlanName, NextToken = NextToken, MaxResults = MaxResults)
  output <- .sagemaker$describe_edge_deployment_plan_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.sagemaker$operations$describe_edge_deployment_plan <- sagemaker_describe_edge_deployment_plan

#' A description of edge packaging jobs
#'
#' @description
#' A description of edge packaging jobs.
#'
#' See [https://www.paws-r-sdk.com/docs/sagemaker_describe_edge_packaging_job/](https://www.paws-r-sdk.com/docs/sagemaker_describe_edge_packaging_job/) for full documentation.
#'
#' @param EdgePackagingJobName &#91;required&#93; The name of the edge packaging job.
#'
#' @keywords internal
#'
#' @rdname sagemaker_describe_edge_packaging_job
sagemaker_describe_edge_packaging_job <- function(EdgePackagingJobName) {
  op <- new_operation(
    name = "DescribeEdgePackagingJob",
    http_method = "POST",
    http_path = "/",
    paginator = list()
  )
  input <- .sagemaker$describe_edge_packaging_job_input(EdgePackagingJobName = EdgePackagingJobName)
  output <- .sagemaker$describe_edge_packaging_job_output()
  config <- get_config()
  svc <- .sagemaker$service(config)
  request <- new_request