R/sagemaker_service.R

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common new_handlers new_service set_config merge_config
NULL

#' Amazon SageMaker Service
#'
#' @description
#' Provides APIs for creating and managing SageMaker resources.
#' 
#' Other Resources:
#' 
#' -   [SageMaker Developer
#'     Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user)
#' 
#' -   [Amazon Augmented AI Runtime API
#'     Reference](https://docs.aws.amazon.com/augmented-ai/2019-11-07/APIReference/Welcome.html)
#'
#' @param
#' config
#' Optional configuration of credentials, endpoint, and/or region.
#' \itemize{
#' \item{\strong{credentials}: \itemize{
#' \item{\strong{creds}: \itemize{
#' \item{\strong{access_key_id}: AWS access key ID}
#' \item{\strong{secret_access_key}: AWS secret access key}
#' \item{\strong{session_token}: AWS temporary session token}
#' }}
#' \item{\strong{profile}: The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}: Set anonymous credentials.}
#' }}
#' \item{\strong{endpoint}: The complete URL to use for the constructed client.}
#' \item{\strong{region}: The AWS Region used in instantiating the client.}
#' \item{\strong{close_connection}: Immediately close all HTTP connections.}
#' \item{\strong{timeout}: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.}
#' \item{\strong{s3_force_path_style}: Set this to `true` to force the request to use path-style addressing, i.e. `http://s3.amazonaws.com/BUCKET/KEY`.}
#' \item{\strong{sts_regional_endpoint}: Set sts regional endpoint resolver to regional or legacy \url{https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html}}
#' }
#' @param
#' credentials
#' Optional credentials shorthand for the config parameter
#' \itemize{
#' \item{\strong{creds}: \itemize{
#' \item{\strong{access_key_id}: AWS access key ID}
#' \item{\strong{secret_access_key}: AWS secret access key}
#' \item{\strong{session_token}: AWS temporary session token}
#' }}
#' \item{\strong{profile}: The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}: Set anonymous credentials.}
#' }
#' @param
#' endpoint
#' Optional shorthand for complete URL to use for the constructed client.
#' @param
#' region
#' Optional shorthand for AWS Region used in instantiating the client.
#'
#' @section Service syntax:
#' ```
#' svc <- sagemaker(
#'   config = list(
#'     credentials = list(
#'       creds = list(
#'         access_key_id = "string",
#'         secret_access_key = "string",
#'         session_token = "string"
#'       ),
#'       profile = "string",
#'       anonymous = "logical"
#'     ),
#'     endpoint = "string",
#'     region = "string",
#'     close_connection = "logical",
#'     timeout = "numeric",
#'     s3_force_path_style = "logical",
#'     sts_regional_endpoint = "string"
#'   ),
#'   credentials = list(
#'     creds = list(
#'       access_key_id = "string",
#'       secret_access_key = "string",
#'       session_token = "string"
#'     ),
#'     profile = "string",
#'     anonymous = "logical"
#'   ),
#'   endpoint = "string",
#'   region = "string"
#' )
#' ```
#'
#' @examples
#' \dontrun{
#' svc <- sagemaker()
#' svc$add_association(
#'   Foo = 123
#' )
#' }
#'
#' @section Operations:
#' \tabular{ll}{
#'  \link[=sagemaker_add_association]{add_association} \tab Creates an association between the source and the destination\cr
#'  \link[=sagemaker_add_tags]{add_tags} \tab Adds or overwrites one or more tags for the specified SageMaker resource\cr
#'  \link[=sagemaker_associate_trial_component]{associate_trial_component} \tab Associates a trial component with a trial\cr
#'  \link[=sagemaker_batch_delete_cluster_nodes]{batch_delete_cluster_nodes} \tab Deletes specific nodes within a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_batch_describe_model_package]{batch_describe_model_package} \tab This action batch describes a list of versioned model packages\cr
#'  \link[=sagemaker_create_action]{create_action} \tab Creates an action\cr
#'  \link[=sagemaker_create_algorithm]{create_algorithm} \tab Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace\cr
#'  \link[=sagemaker_create_app]{create_app} \tab Creates a running app for the specified UserProfile\cr
#'  \link[=sagemaker_create_app_image_config]{create_app_image_config} \tab Creates a configuration for running a SageMaker AI image as a KernelGateway app\cr
#'  \link[=sagemaker_create_artifact]{create_artifact} \tab Creates an artifact\cr
#'  \link[=sagemaker_create_auto_ml_job]{create_auto_ml_job} \tab Creates an Autopilot job also referred to as Autopilot experiment or AutoML job\cr
#'  \link[=sagemaker_create_auto_ml_job_v2]{create_auto_ml_job_v2} \tab Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2\cr
#'  \link[=sagemaker_create_cluster]{create_cluster} \tab Creates a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_create_cluster_scheduler_config]{create_cluster_scheduler_config} \tab Create cluster policy configuration\cr
#'  \link[=sagemaker_create_code_repository]{create_code_repository} \tab Creates a Git repository as a resource in your SageMaker AI account\cr
#'  \link[=sagemaker_create_compilation_job]{create_compilation_job} \tab Starts a model compilation job\cr
#'  \link[=sagemaker_create_compute_quota]{create_compute_quota} \tab Create compute allocation definition\cr
#'  \link[=sagemaker_create_context]{create_context} \tab Creates a context\cr
#'  \link[=sagemaker_create_data_quality_job_definition]{create_data_quality_job_definition} \tab Creates a definition for a job that monitors data quality and drift\cr
#'  \link[=sagemaker_create_device_fleet]{create_device_fleet} \tab Creates a device fleet\cr
#'  \link[=sagemaker_create_domain]{create_domain} \tab Creates a Domain\cr
#'  \link[=sagemaker_create_edge_deployment_plan]{create_edge_deployment_plan} \tab Creates an edge deployment plan, consisting of multiple stages\cr
#'  \link[=sagemaker_create_edge_deployment_stage]{create_edge_deployment_stage} \tab Creates a new stage in an existing edge deployment plan\cr
#'  \link[=sagemaker_create_edge_packaging_job]{create_edge_packaging_job} \tab Starts a SageMaker Edge Manager model packaging job\cr
#'  \link[=sagemaker_create_endpoint]{create_endpoint} \tab Creates an endpoint using the endpoint configuration specified in the request\cr
#'  \link[=sagemaker_create_endpoint_config]{create_endpoint_config} \tab Creates an endpoint configuration that SageMaker hosting services uses to deploy models\cr
#'  \link[=sagemaker_create_experiment]{create_experiment} \tab Creates a SageMaker experiment\cr
#'  \link[=sagemaker_create_feature_group]{create_feature_group} \tab Create a new FeatureGroup\cr
#'  \link[=sagemaker_create_flow_definition]{create_flow_definition} \tab Creates a flow definition\cr
#'  \link[=sagemaker_create_hub]{create_hub} \tab Create a hub\cr
#'  \link[=sagemaker_create_hub_content_reference]{create_hub_content_reference} \tab Create a hub content reference in order to add a model in the JumpStart public hub to a private hub\cr
#'  \link[=sagemaker_create_human_task_ui]{create_human_task_ui} \tab Defines the settings you will use for the human review workflow user interface\cr
#'  \link[=sagemaker_create_hyper_parameter_tuning_job]{create_hyper_parameter_tuning_job} \tab Starts a hyperparameter tuning job\cr
#'  \link[=sagemaker_create_image]{create_image} \tab Creates a custom SageMaker AI image\cr
#'  \link[=sagemaker_create_image_version]{create_image_version} \tab Creates a version of the SageMaker AI image specified by ImageName\cr
#'  \link[=sagemaker_create_inference_component]{create_inference_component} \tab Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint\cr
#'  \link[=sagemaker_create_inference_experiment]{create_inference_experiment} \tab Creates an inference experiment using the configurations specified in the request\cr
#'  \link[=sagemaker_create_inference_recommendations_job]{create_inference_recommendations_job} \tab Starts a recommendation job\cr
#'  \link[=sagemaker_create_labeling_job]{create_labeling_job} \tab Creates a job that uses workers to label the data objects in your input dataset\cr
#'  \link[=sagemaker_create_mlflow_tracking_server]{create_mlflow_tracking_server} \tab Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store\cr
#'  \link[=sagemaker_create_model]{create_model} \tab Creates a model in SageMaker\cr
#'  \link[=sagemaker_create_model_bias_job_definition]{create_model_bias_job_definition} \tab Creates the definition for a model bias job\cr
#'  \link[=sagemaker_create_model_card]{create_model_card} \tab Creates an Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_create_model_card_export_job]{create_model_card_export_job} \tab Creates an Amazon SageMaker Model Card export job\cr
#'  \link[=sagemaker_create_model_explainability_job_definition]{create_model_explainability_job_definition} \tab Creates the definition for a model explainability job\cr
#'  \link[=sagemaker_create_model_package]{create_model_package} \tab 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\cr
#'  \link[=sagemaker_create_model_package_group]{create_model_package_group} \tab Creates a model group\cr
#'  \link[=sagemaker_create_model_quality_job_definition]{create_model_quality_job_definition} \tab Creates a definition for a job that monitors model quality and drift\cr
#'  \link[=sagemaker_create_monitoring_schedule]{create_monitoring_schedule} \tab Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint\cr
#'  \link[=sagemaker_create_notebook_instance]{create_notebook_instance} \tab Creates an SageMaker AI notebook instance\cr
#'  \link[=sagemaker_create_notebook_instance_lifecycle_config]{create_notebook_instance_lifecycle_config} \tab Creates a lifecycle configuration that you can associate with a notebook instance\cr
#'  \link[=sagemaker_create_optimization_job]{create_optimization_job} \tab Creates a job that optimizes a model for inference performance\cr
#'  \link[=sagemaker_create_partner_app]{create_partner_app} \tab Creates an Amazon SageMaker Partner AI App\cr
#'  \link[=sagemaker_create_partner_app_presigned_url]{create_partner_app_presigned_url} \tab Creates a presigned URL to access an Amazon SageMaker Partner AI App\cr
#'  \link[=sagemaker_create_pipeline]{create_pipeline} \tab Creates a pipeline using a JSON pipeline definition\cr
#'  \link[=sagemaker_create_presigned_domain_url]{create_presigned_domain_url} \tab Creates a URL for a specified UserProfile in a Domain\cr
#'  \link[=sagemaker_create_presigned_mlflow_tracking_server_url]{create_presigned_mlflow_tracking_server_url} \tab Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server\cr
#'  \link[=sagemaker_create_presigned_notebook_instance_url]{create_presigned_notebook_instance_url} \tab Returns a URL that you can use to connect to the Jupyter server from a notebook instance\cr
#'  \link[=sagemaker_create_processing_job]{create_processing_job} \tab Creates a processing job\cr
#'  \link[=sagemaker_create_project]{create_project} \tab 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\cr
#'  \link[=sagemaker_create_space]{create_space} \tab Creates a private space or a space used for real time collaboration in a domain\cr
#'  \link[=sagemaker_create_studio_lifecycle_config]{create_studio_lifecycle_config} \tab Creates a new Amazon SageMaker AI Studio Lifecycle Configuration\cr
#'  \link[=sagemaker_create_training_job]{create_training_job} \tab Starts a model training job\cr
#'  \link[=sagemaker_create_training_plan]{create_training_plan} \tab Creates a new training plan in SageMaker to reserve compute capacity\cr
#'  \link[=sagemaker_create_transform_job]{create_transform_job} \tab Starts a transform job\cr
#'  \link[=sagemaker_create_trial]{create_trial} \tab Creates an SageMaker trial\cr
#'  \link[=sagemaker_create_trial_component]{create_trial_component} \tab Creates a trial component, which is a stage of a machine learning trial\cr
#'  \link[=sagemaker_create_user_profile]{create_user_profile} \tab Creates a user profile\cr
#'  \link[=sagemaker_create_workforce]{create_workforce} \tab Use this operation to create a workforce\cr
#'  \link[=sagemaker_create_workteam]{create_workteam} \tab Creates a new work team for labeling your data\cr
#'  \link[=sagemaker_delete_action]{delete_action} \tab Deletes an action\cr
#'  \link[=sagemaker_delete_algorithm]{delete_algorithm} \tab Removes the specified algorithm from your account\cr
#'  \link[=sagemaker_delete_app]{delete_app} \tab Used to stop and delete an app\cr
#'  \link[=sagemaker_delete_app_image_config]{delete_app_image_config} \tab Deletes an AppImageConfig\cr
#'  \link[=sagemaker_delete_artifact]{delete_artifact} \tab Deletes an artifact\cr
#'  \link[=sagemaker_delete_association]{delete_association} \tab Deletes an association\cr
#'  \link[=sagemaker_delete_cluster]{delete_cluster} \tab Delete a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_delete_cluster_scheduler_config]{delete_cluster_scheduler_config} \tab Deletes the cluster policy of the cluster\cr
#'  \link[=sagemaker_delete_code_repository]{delete_code_repository} \tab Deletes the specified Git repository from your account\cr
#'  \link[=sagemaker_delete_compilation_job]{delete_compilation_job} \tab Deletes the specified compilation job\cr
#'  \link[=sagemaker_delete_compute_quota]{delete_compute_quota} \tab Deletes the compute allocation from the cluster\cr
#'  \link[=sagemaker_delete_context]{delete_context} \tab Deletes an context\cr
#'  \link[=sagemaker_delete_data_quality_job_definition]{delete_data_quality_job_definition} \tab Deletes a data quality monitoring job definition\cr
#'  \link[=sagemaker_delete_device_fleet]{delete_device_fleet} \tab Deletes a fleet\cr
#'  \link[=sagemaker_delete_domain]{delete_domain} \tab Used to delete a domain\cr
#'  \link[=sagemaker_delete_edge_deployment_plan]{delete_edge_deployment_plan} \tab 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\cr
#'  \link[=sagemaker_delete_edge_deployment_stage]{delete_edge_deployment_stage} \tab Delete a stage in an edge deployment plan if (and only if) the stage is inactive\cr
#'  \link[=sagemaker_delete_endpoint]{delete_endpoint} \tab Deletes an endpoint\cr
#'  \link[=sagemaker_delete_endpoint_config]{delete_endpoint_config} \tab Deletes an endpoint configuration\cr
#'  \link[=sagemaker_delete_experiment]{delete_experiment} \tab Deletes an SageMaker experiment\cr
#'  \link[=sagemaker_delete_feature_group]{delete_feature_group} \tab Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup\cr
#'  \link[=sagemaker_delete_flow_definition]{delete_flow_definition} \tab Deletes the specified flow definition\cr
#'  \link[=sagemaker_delete_hub]{delete_hub} \tab Delete a hub\cr
#'  \link[=sagemaker_delete_hub_content]{delete_hub_content} \tab Delete the contents of a hub\cr
#'  \link[=sagemaker_delete_hub_content_reference]{delete_hub_content_reference} \tab Delete a hub content reference in order to remove a model from a private hub\cr
#'  \link[=sagemaker_delete_human_task_ui]{delete_human_task_ui} \tab Use this operation to delete a human task user interface (worker task template)\cr
#'  \link[=sagemaker_delete_hyper_parameter_tuning_job]{delete_hyper_parameter_tuning_job} \tab Deletes a hyperparameter tuning job\cr
#'  \link[=sagemaker_delete_image]{delete_image} \tab Deletes a SageMaker AI image and all versions of the image\cr
#'  \link[=sagemaker_delete_image_version]{delete_image_version} \tab Deletes a version of a SageMaker AI image\cr
#'  \link[=sagemaker_delete_inference_component]{delete_inference_component} \tab Deletes an inference component\cr
#'  \link[=sagemaker_delete_inference_experiment]{delete_inference_experiment} \tab Deletes an inference experiment\cr
#'  \link[=sagemaker_delete_mlflow_tracking_server]{delete_mlflow_tracking_server} \tab Deletes an MLflow Tracking Server\cr
#'  \link[=sagemaker_delete_model]{delete_model} \tab Deletes a model\cr
#'  \link[=sagemaker_delete_model_bias_job_definition]{delete_model_bias_job_definition} \tab Deletes an Amazon SageMaker AI model bias job definition\cr
#'  \link[=sagemaker_delete_model_card]{delete_model_card} \tab Deletes an Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_delete_model_explainability_job_definition]{delete_model_explainability_job_definition} \tab Deletes an Amazon SageMaker AI model explainability job definition\cr
#'  \link[=sagemaker_delete_model_package]{delete_model_package} \tab Deletes a model package\cr
#'  \link[=sagemaker_delete_model_package_group]{delete_model_package_group} \tab Deletes the specified model group\cr
#'  \link[=sagemaker_delete_model_package_group_policy]{delete_model_package_group_policy} \tab Deletes a model group resource policy\cr
#'  \link[=sagemaker_delete_model_quality_job_definition]{delete_model_quality_job_definition} \tab Deletes the secified model quality monitoring job definition\cr
#'  \link[=sagemaker_delete_monitoring_schedule]{delete_monitoring_schedule} \tab Deletes a monitoring schedule\cr
#'  \link[=sagemaker_delete_notebook_instance]{delete_notebook_instance} \tab Deletes an SageMaker AI notebook instance\cr
#'  \link[=sagemaker_delete_notebook_instance_lifecycle_config]{delete_notebook_instance_lifecycle_config} \tab Deletes a notebook instance lifecycle configuration\cr
#'  \link[=sagemaker_delete_optimization_job]{delete_optimization_job} \tab Deletes an optimization job\cr
#'  \link[=sagemaker_delete_partner_app]{delete_partner_app} \tab Deletes a SageMaker Partner AI App\cr
#'  \link[=sagemaker_delete_pipeline]{delete_pipeline} \tab Deletes a pipeline if there are no running instances of the pipeline\cr
#'  \link[=sagemaker_delete_project]{delete_project} \tab Delete the specified project\cr
#'  \link[=sagemaker_delete_space]{delete_space} \tab Used to delete a space\cr
#'  \link[=sagemaker_delete_studio_lifecycle_config]{delete_studio_lifecycle_config} \tab Deletes the Amazon SageMaker AI Studio Lifecycle Configuration\cr
#'  \link[=sagemaker_delete_tags]{delete_tags} \tab Deletes the specified tags from an SageMaker resource\cr
#'  \link[=sagemaker_delete_trial]{delete_trial} \tab Deletes the specified trial\cr
#'  \link[=sagemaker_delete_trial_component]{delete_trial_component} \tab Deletes the specified trial component\cr
#'  \link[=sagemaker_delete_user_profile]{delete_user_profile} \tab Deletes a user profile\cr
#'  \link[=sagemaker_delete_workforce]{delete_workforce} \tab Use this operation to delete a workforce\cr
#'  \link[=sagemaker_delete_workteam]{delete_workteam} \tab Deletes an existing work team\cr
#'  \link[=sagemaker_deregister_devices]{deregister_devices} \tab Deregisters the specified devices\cr
#'  \link[=sagemaker_describe_action]{describe_action} \tab Describes an action\cr
#'  \link[=sagemaker_describe_algorithm]{describe_algorithm} \tab Returns a description of the specified algorithm that is in your account\cr
#'  \link[=sagemaker_describe_app]{describe_app} \tab Describes the app\cr
#'  \link[=sagemaker_describe_app_image_config]{describe_app_image_config} \tab Describes an AppImageConfig\cr
#'  \link[=sagemaker_describe_artifact]{describe_artifact} \tab Describes an artifact\cr
#'  \link[=sagemaker_describe_auto_ml_job]{describe_auto_ml_job} \tab Returns information about an AutoML job created by calling CreateAutoMLJob\cr
#'  \link[=sagemaker_describe_auto_ml_job_v2]{describe_auto_ml_job_v2} \tab Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob\cr
#'  \link[=sagemaker_describe_cluster]{describe_cluster} \tab Retrieves information of a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_describe_cluster_node]{describe_cluster_node} \tab Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_describe_cluster_scheduler_config]{describe_cluster_scheduler_config} \tab Description of the cluster policy\cr
#'  \link[=sagemaker_describe_code_repository]{describe_code_repository} \tab Gets details about the specified Git repository\cr
#'  \link[=sagemaker_describe_compilation_job]{describe_compilation_job} \tab Returns information about a model compilation job\cr
#'  \link[=sagemaker_describe_compute_quota]{describe_compute_quota} \tab Description of the compute allocation definition\cr
#'  \link[=sagemaker_describe_context]{describe_context} \tab Describes a context\cr
#'  \link[=sagemaker_describe_data_quality_job_definition]{describe_data_quality_job_definition} \tab Gets the details of a data quality monitoring job definition\cr
#'  \link[=sagemaker_describe_device]{describe_device} \tab Describes the device\cr
#'  \link[=sagemaker_describe_device_fleet]{describe_device_fleet} \tab A description of the fleet the device belongs to\cr
#'  \link[=sagemaker_describe_domain]{describe_domain} \tab The description of the domain\cr
#'  \link[=sagemaker_describe_edge_deployment_plan]{describe_edge_deployment_plan} \tab Describes an edge deployment plan with deployment status per stage\cr
#'  \link[=sagemaker_describe_edge_packaging_job]{describe_edge_packaging_job} \tab A description of edge packaging jobs\cr
#'  \link[=sagemaker_describe_endpoint]{describe_endpoint} \tab Returns the description of an endpoint\cr
#'  \link[=sagemaker_describe_endpoint_config]{describe_endpoint_config} \tab Returns the description of an endpoint configuration created using the CreateEndpointConfig API\cr
#'  \link[=sagemaker_describe_experiment]{describe_experiment} \tab Provides a list of an experiment's properties\cr
#'  \link[=sagemaker_describe_feature_group]{describe_feature_group} \tab Use this operation to describe a FeatureGroup\cr
#'  \link[=sagemaker_describe_feature_metadata]{describe_feature_metadata} \tab Shows the metadata for a feature within a feature group\cr
#'  \link[=sagemaker_describe_flow_definition]{describe_flow_definition} \tab Returns information about the specified flow definition\cr
#'  \link[=sagemaker_describe_hub]{describe_hub} \tab Describes a hub\cr
#'  \link[=sagemaker_describe_hub_content]{describe_hub_content} \tab Describe the content of a hub\cr
#'  \link[=sagemaker_describe_human_task_ui]{describe_human_task_ui} \tab Returns information about the requested human task user interface (worker task template)\cr
#'  \link[=sagemaker_describe_hyper_parameter_tuning_job]{describe_hyper_parameter_tuning_job} \tab Returns a description of a hyperparameter tuning job, depending on the fields selected\cr
#'  \link[=sagemaker_describe_image]{describe_image} \tab Describes a SageMaker AI image\cr
#'  \link[=sagemaker_describe_image_version]{describe_image_version} \tab Describes a version of a SageMaker AI image\cr
#'  \link[=sagemaker_describe_inference_component]{describe_inference_component} \tab Returns information about an inference component\cr
#'  \link[=sagemaker_describe_inference_experiment]{describe_inference_experiment} \tab Returns details about an inference experiment\cr
#'  \link[=sagemaker_describe_inference_recommendations_job]{describe_inference_recommendations_job} \tab Provides the results of the Inference Recommender job\cr
#'  \link[=sagemaker_describe_labeling_job]{describe_labeling_job} \tab Gets information about a labeling job\cr
#'  \link[=sagemaker_describe_lineage_group]{describe_lineage_group} \tab Provides a list of properties for the requested lineage group\cr
#'  \link[=sagemaker_describe_mlflow_tracking_server]{describe_mlflow_tracking_server} \tab Returns information about an MLflow Tracking Server\cr
#'  \link[=sagemaker_describe_model]{describe_model} \tab Describes a model that you created using the CreateModel API\cr
#'  \link[=sagemaker_describe_model_bias_job_definition]{describe_model_bias_job_definition} \tab Returns a description of a model bias job definition\cr
#'  \link[=sagemaker_describe_model_card]{describe_model_card} \tab Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_describe_model_card_export_job]{describe_model_card_export_job} \tab Describes an Amazon SageMaker Model Card export job\cr
#'  \link[=sagemaker_describe_model_explainability_job_definition]{describe_model_explainability_job_definition} \tab Returns a description of a model explainability job definition\cr
#'  \link[=sagemaker_describe_model_package]{describe_model_package} \tab Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace\cr
#'  \link[=sagemaker_describe_model_package_group]{describe_model_package_group} \tab Gets a description for the specified model group\cr
#'  \link[=sagemaker_describe_model_quality_job_definition]{describe_model_quality_job_definition} \tab Returns a description of a model quality job definition\cr
#'  \link[=sagemaker_describe_monitoring_schedule]{describe_monitoring_schedule} \tab Describes the schedule for a monitoring job\cr
#'  \link[=sagemaker_describe_notebook_instance]{describe_notebook_instance} \tab Returns information about a notebook instance\cr
#'  \link[=sagemaker_describe_notebook_instance_lifecycle_config]{describe_notebook_instance_lifecycle_config} \tab Returns a description of a notebook instance lifecycle configuration\cr
#'  \link[=sagemaker_describe_optimization_job]{describe_optimization_job} \tab Provides the properties of the specified optimization job\cr
#'  \link[=sagemaker_describe_partner_app]{describe_partner_app} \tab Gets information about a SageMaker Partner AI App\cr
#'  \link[=sagemaker_describe_pipeline]{describe_pipeline} \tab Describes the details of a pipeline\cr
#'  \link[=sagemaker_describe_pipeline_definition_for_execution]{describe_pipeline_definition_for_execution} \tab Describes the details of an execution's pipeline definition\cr
#'  \link[=sagemaker_describe_pipeline_execution]{describe_pipeline_execution} \tab Describes the details of a pipeline execution\cr
#'  \link[=sagemaker_describe_processing_job]{describe_processing_job} \tab Returns a description of a processing job\cr
#'  \link[=sagemaker_describe_project]{describe_project} \tab Describes the details of a project\cr
#'  \link[=sagemaker_describe_space]{describe_space} \tab Describes the space\cr
#'  \link[=sagemaker_describe_studio_lifecycle_config]{describe_studio_lifecycle_config} \tab Describes the Amazon SageMaker AI Studio Lifecycle Configuration\cr
#'  \link[=sagemaker_describe_subscribed_workteam]{describe_subscribed_workteam} \tab Gets information about a work team provided by a vendor\cr
#'  \link[=sagemaker_describe_training_job]{describe_training_job} \tab Returns information about a training job\cr
#'  \link[=sagemaker_describe_training_plan]{describe_training_plan} \tab Retrieves detailed information about a specific training plan\cr
#'  \link[=sagemaker_describe_transform_job]{describe_transform_job} \tab Returns information about a transform job\cr
#'  \link[=sagemaker_describe_trial]{describe_trial} \tab Provides a list of a trial's properties\cr
#'  \link[=sagemaker_describe_trial_component]{describe_trial_component} \tab Provides a list of a trials component's properties\cr
#'  \link[=sagemaker_describe_user_profile]{describe_user_profile} \tab Describes a user profile\cr
#'  \link[=sagemaker_describe_workforce]{describe_workforce} \tab Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs)\cr
#'  \link[=sagemaker_describe_workteam]{describe_workteam} \tab Gets information about a specific work team\cr
#'  \link[=sagemaker_disable_sagemaker_servicecatalog_portfolio]{disable_sagemaker_servicecatalog_portfolio} \tab Disables using Service Catalog in SageMaker\cr
#'  \link[=sagemaker_disassociate_trial_component]{disassociate_trial_component} \tab Disassociates a trial component from a trial\cr
#'  \link[=sagemaker_enable_sagemaker_servicecatalog_portfolio]{enable_sagemaker_servicecatalog_portfolio} \tab Enables using Service Catalog in SageMaker\cr
#'  \link[=sagemaker_get_device_fleet_report]{get_device_fleet_report} \tab Describes a fleet\cr
#'  \link[=sagemaker_get_lineage_group_policy]{get_lineage_group_policy} \tab The resource policy for the lineage group\cr
#'  \link[=sagemaker_get_model_package_group_policy]{get_model_package_group_policy} \tab Gets a resource policy that manages access for a model group\cr
#'  \link[=sagemaker_get_sagemaker_servicecatalog_portfolio_status]{get_sagemaker_servicecatalog_portfolio_status} \tab Gets the status of Service Catalog in SageMaker\cr
#'  \link[=sagemaker_get_scaling_configuration_recommendation]{get_scaling_configuration_recommendation} \tab Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job\cr
#'  \link[=sagemaker_get_search_suggestions]{get_search_suggestions} \tab An auto-complete API for the search functionality in the SageMaker console\cr
#'  \link[=sagemaker_import_hub_content]{import_hub_content} \tab Import hub content\cr
#'  \link[=sagemaker_list_actions]{list_actions} \tab Lists the actions in your account and their properties\cr
#'  \link[=sagemaker_list_algorithms]{list_algorithms} \tab Lists the machine learning algorithms that have been created\cr
#'  \link[=sagemaker_list_aliases]{list_aliases} \tab Lists the aliases of a specified image or image version\cr
#'  \link[=sagemaker_list_app_image_configs]{list_app_image_configs} \tab Lists the AppImageConfigs in your account and their properties\cr
#'  \link[=sagemaker_list_apps]{list_apps} \tab Lists apps\cr
#'  \link[=sagemaker_list_artifacts]{list_artifacts} \tab Lists the artifacts in your account and their properties\cr
#'  \link[=sagemaker_list_associations]{list_associations} \tab Lists the associations in your account and their properties\cr
#'  \link[=sagemaker_list_auto_ml_jobs]{list_auto_ml_jobs} \tab Request a list of jobs\cr
#'  \link[=sagemaker_list_candidates_for_auto_ml_job]{list_candidates_for_auto_ml_job} \tab List the candidates created for the job\cr
#'  \link[=sagemaker_list_cluster_nodes]{list_cluster_nodes} \tab Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_list_clusters]{list_clusters} \tab Retrieves the list of SageMaker HyperPod clusters\cr
#'  \link[=sagemaker_list_cluster_scheduler_configs]{list_cluster_scheduler_configs} \tab List the cluster policy configurations\cr
#'  \link[=sagemaker_list_code_repositories]{list_code_repositories} \tab Gets a list of the Git repositories in your account\cr
#'  \link[=sagemaker_list_compilation_jobs]{list_compilation_jobs} \tab Lists model compilation jobs that satisfy various filters\cr
#'  \link[=sagemaker_list_compute_quotas]{list_compute_quotas} \tab List the resource allocation definitions\cr
#'  \link[=sagemaker_list_contexts]{list_contexts} \tab Lists the contexts in your account and their properties\cr
#'  \link[=sagemaker_list_data_quality_job_definitions]{list_data_quality_job_definitions} \tab Lists the data quality job definitions in your account\cr
#'  \link[=sagemaker_list_device_fleets]{list_device_fleets} \tab Returns a list of devices in the fleet\cr
#'  \link[=sagemaker_list_devices]{list_devices} \tab A list of devices\cr
#'  \link[=sagemaker_list_domains]{list_domains} \tab Lists the domains\cr
#'  \link[=sagemaker_list_edge_deployment_plans]{list_edge_deployment_plans} \tab Lists all edge deployment plans\cr
#'  \link[=sagemaker_list_edge_packaging_jobs]{list_edge_packaging_jobs} \tab Returns a list of edge packaging jobs\cr
#'  \link[=sagemaker_list_endpoint_configs]{list_endpoint_configs} \tab Lists endpoint configurations\cr
#'  \link[=sagemaker_list_endpoints]{list_endpoints} \tab Lists endpoints\cr
#'  \link[=sagemaker_list_experiments]{list_experiments} \tab Lists all the experiments in your account\cr
#'  \link[=sagemaker_list_feature_groups]{list_feature_groups} \tab List FeatureGroups based on given filter and order\cr
#'  \link[=sagemaker_list_flow_definitions]{list_flow_definitions} \tab Returns information about the flow definitions in your account\cr
#'  \link[=sagemaker_list_hub_contents]{list_hub_contents} \tab List the contents of a hub\cr
#'  \link[=sagemaker_list_hub_content_versions]{list_hub_content_versions} \tab List hub content versions\cr
#'  \link[=sagemaker_list_hubs]{list_hubs} \tab List all existing hubs\cr
#'  \link[=sagemaker_list_human_task_uis]{list_human_task_uis} \tab Returns information about the human task user interfaces in your account\cr
#'  \link[=sagemaker_list_hyper_parameter_tuning_jobs]{list_hyper_parameter_tuning_jobs} \tab Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account\cr
#'  \link[=sagemaker_list_images]{list_images} \tab Lists the images in your account and their properties\cr
#'  \link[=sagemaker_list_image_versions]{list_image_versions} \tab Lists the versions of a specified image and their properties\cr
#'  \link[=sagemaker_list_inference_components]{list_inference_components} \tab Lists the inference components in your account and their properties\cr
#'  \link[=sagemaker_list_inference_experiments]{list_inference_experiments} \tab Returns the list of all inference experiments\cr
#'  \link[=sagemaker_list_inference_recommendations_jobs]{list_inference_recommendations_jobs} \tab Lists recommendation jobs that satisfy various filters\cr
#'  \link[=sagemaker_list_inference_recommendations_job_steps]{list_inference_recommendations_job_steps} \tab Returns a list of the subtasks for an Inference Recommender job\cr
#'  \link[=sagemaker_list_labeling_jobs]{list_labeling_jobs} \tab Gets a list of labeling jobs\cr
#'  \link[=sagemaker_list_labeling_jobs_for_workteam]{list_labeling_jobs_for_workteam} \tab Gets a list of labeling jobs assigned to a specified work team\cr
#'  \link[=sagemaker_list_lineage_groups]{list_lineage_groups} \tab A list of lineage groups shared with your Amazon Web Services account\cr
#'  \link[=sagemaker_list_mlflow_tracking_servers]{list_mlflow_tracking_servers} \tab Lists all MLflow Tracking Servers\cr
#'  \link[=sagemaker_list_model_bias_job_definitions]{list_model_bias_job_definitions} \tab Lists model bias jobs definitions that satisfy various filters\cr
#'  \link[=sagemaker_list_model_card_export_jobs]{list_model_card_export_jobs} \tab List the export jobs for the Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_list_model_cards]{list_model_cards} \tab List existing model cards\cr
#'  \link[=sagemaker_list_model_card_versions]{list_model_card_versions} \tab List existing versions of an Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_list_model_explainability_job_definitions]{list_model_explainability_job_definitions} \tab Lists model explainability job definitions that satisfy various filters\cr
#'  \link[=sagemaker_list_model_metadata]{list_model_metadata} \tab Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos\cr
#'  \link[=sagemaker_list_model_package_groups]{list_model_package_groups} \tab Gets a list of the model groups in your Amazon Web Services account\cr
#'  \link[=sagemaker_list_model_packages]{list_model_packages} \tab Lists the model packages that have been created\cr
#'  \link[=sagemaker_list_model_quality_job_definitions]{list_model_quality_job_definitions} \tab Gets a list of model quality monitoring job definitions in your account\cr
#'  \link[=sagemaker_list_models]{list_models} \tab Lists models created with the CreateModel API\cr
#'  \link[=sagemaker_list_monitoring_alert_history]{list_monitoring_alert_history} \tab Gets a list of past alerts in a model monitoring schedule\cr
#'  \link[=sagemaker_list_monitoring_alerts]{list_monitoring_alerts} \tab Gets the alerts for a single monitoring schedule\cr
#'  \link[=sagemaker_list_monitoring_executions]{list_monitoring_executions} \tab Returns list of all monitoring job executions\cr
#'  \link[=sagemaker_list_monitoring_schedules]{list_monitoring_schedules} \tab Returns list of all monitoring schedules\cr
#'  \link[=sagemaker_list_notebook_instance_lifecycle_configs]{list_notebook_instance_lifecycle_configs} \tab Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API\cr
#'  \link[=sagemaker_list_notebook_instances]{list_notebook_instances} \tab Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region\cr
#'  \link[=sagemaker_list_optimization_jobs]{list_optimization_jobs} \tab Lists the optimization jobs in your account and their properties\cr
#'  \link[=sagemaker_list_partner_apps]{list_partner_apps} \tab Lists all of the SageMaker Partner AI Apps in an account\cr
#'  \link[=sagemaker_list_pipeline_executions]{list_pipeline_executions} \tab Gets a list of the pipeline executions\cr
#'  \link[=sagemaker_list_pipeline_execution_steps]{list_pipeline_execution_steps} \tab Gets a list of PipeLineExecutionStep objects\cr
#'  \link[=sagemaker_list_pipeline_parameters_for_execution]{list_pipeline_parameters_for_execution} \tab Gets a list of parameters for a pipeline execution\cr
#'  \link[=sagemaker_list_pipelines]{list_pipelines} \tab Gets a list of pipelines\cr
#'  \link[=sagemaker_list_processing_jobs]{list_processing_jobs} \tab Lists processing jobs that satisfy various filters\cr
#'  \link[=sagemaker_list_projects]{list_projects} \tab Gets a list of the projects in an Amazon Web Services account\cr
#'  \link[=sagemaker_list_resource_catalogs]{list_resource_catalogs} \tab Lists Amazon SageMaker Catalogs based on given filters and orders\cr
#'  \link[=sagemaker_list_spaces]{list_spaces} \tab Lists spaces\cr
#'  \link[=sagemaker_list_stage_devices]{list_stage_devices} \tab Lists devices allocated to the stage, containing detailed device information and deployment status\cr
#'  \link[=sagemaker_list_studio_lifecycle_configs]{list_studio_lifecycle_configs} \tab Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account\cr
#'  \link[=sagemaker_list_subscribed_workteams]{list_subscribed_workteams} \tab Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace\cr
#'  \link[=sagemaker_list_tags]{list_tags} \tab Returns the tags for the specified SageMaker resource\cr
#'  \link[=sagemaker_list_training_jobs]{list_training_jobs} \tab Lists training jobs\cr
#'  \link[=sagemaker_list_training_jobs_for_hyper_parameter_tuning_job]{list_training_jobs_for_hyper_parameter_tuning_job} \tab Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched\cr
#'  \link[=sagemaker_list_training_plans]{list_training_plans} \tab Retrieves a list of training plans for the current account\cr
#'  \link[=sagemaker_list_transform_jobs]{list_transform_jobs} \tab Lists transform jobs\cr
#'  \link[=sagemaker_list_trial_components]{list_trial_components} \tab Lists the trial components in your account\cr
#'  \link[=sagemaker_list_trials]{list_trials} \tab Lists the trials in your account\cr
#'  \link[=sagemaker_list_user_profiles]{list_user_profiles} \tab Lists user profiles\cr
#'  \link[=sagemaker_list_workforces]{list_workforces} \tab Use this operation to list all private and vendor workforces in an Amazon Web Services Region\cr
#'  \link[=sagemaker_list_workteams]{list_workteams} \tab Gets a list of private work teams that you have defined in a region\cr
#'  \link[=sagemaker_put_model_package_group_policy]{put_model_package_group_policy} \tab Adds a resouce policy to control access to a model group\cr
#'  \link[=sagemaker_query_lineage]{query_lineage} \tab Use this action to inspect your lineage and discover relationships between entities\cr
#'  \link[=sagemaker_register_devices]{register_devices} \tab Register devices\cr
#'  \link[=sagemaker_render_ui_template]{render_ui_template} \tab Renders the UI template so that you can preview the worker's experience\cr
#'  \link[=sagemaker_retry_pipeline_execution]{retry_pipeline_execution} \tab Retry the execution of the pipeline\cr
#'  \link[=sagemaker_search]{search} \tab Finds SageMaker resources that match a search query\cr
#'  \link[=sagemaker_search_training_plan_offerings]{search_training_plan_offerings} \tab Searches for available training plan offerings based on specified criteria\cr
#'  \link[=sagemaker_send_pipeline_execution_step_failure]{send_pipeline_execution_step_failure} \tab Notifies the pipeline that the execution of a callback step failed, along with a message describing why\cr
#'  \link[=sagemaker_send_pipeline_execution_step_success]{send_pipeline_execution_step_success} \tab Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters\cr
#'  \link[=sagemaker_start_edge_deployment_stage]{start_edge_deployment_stage} \tab Starts a stage in an edge deployment plan\cr
#'  \link[=sagemaker_start_inference_experiment]{start_inference_experiment} \tab Starts an inference experiment\cr
#'  \link[=sagemaker_start_mlflow_tracking_server]{start_mlflow_tracking_server} \tab Programmatically start an MLflow Tracking Server\cr
#'  \link[=sagemaker_start_monitoring_schedule]{start_monitoring_schedule} \tab Starts a previously stopped monitoring schedule\cr
#'  \link[=sagemaker_start_notebook_instance]{start_notebook_instance} \tab Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume\cr
#'  \link[=sagemaker_start_pipeline_execution]{start_pipeline_execution} \tab Starts a pipeline execution\cr
#'  \link[=sagemaker_stop_auto_ml_job]{stop_auto_ml_job} \tab A method for forcing a running job to shut down\cr
#'  \link[=sagemaker_stop_compilation_job]{stop_compilation_job} \tab Stops a model compilation job\cr
#'  \link[=sagemaker_stop_edge_deployment_stage]{stop_edge_deployment_stage} \tab Stops a stage in an edge deployment plan\cr
#'  \link[=sagemaker_stop_edge_packaging_job]{stop_edge_packaging_job} \tab Request to stop an edge packaging job\cr
#'  \link[=sagemaker_stop_hyper_parameter_tuning_job]{stop_hyper_parameter_tuning_job} \tab Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched\cr
#'  \link[=sagemaker_stop_inference_experiment]{stop_inference_experiment} \tab Stops an inference experiment\cr
#'  \link[=sagemaker_stop_inference_recommendations_job]{stop_inference_recommendations_job} \tab Stops an Inference Recommender job\cr
#'  \link[=sagemaker_stop_labeling_job]{stop_labeling_job} \tab Stops a running labeling job\cr
#'  \link[=sagemaker_stop_mlflow_tracking_server]{stop_mlflow_tracking_server} \tab Programmatically stop an MLflow Tracking Server\cr
#'  \link[=sagemaker_stop_monitoring_schedule]{stop_monitoring_schedule} \tab Stops a previously started monitoring schedule\cr
#'  \link[=sagemaker_stop_notebook_instance]{stop_notebook_instance} \tab Terminates the ML compute instance\cr
#'  \link[=sagemaker_stop_optimization_job]{stop_optimization_job} \tab Ends a running inference optimization job\cr
#'  \link[=sagemaker_stop_pipeline_execution]{stop_pipeline_execution} \tab Stops a pipeline execution\cr
#'  \link[=sagemaker_stop_processing_job]{stop_processing_job} \tab Stops a processing job\cr
#'  \link[=sagemaker_stop_training_job]{stop_training_job} \tab Stops a training job\cr
#'  \link[=sagemaker_stop_transform_job]{stop_transform_job} \tab Stops a batch transform job\cr
#'  \link[=sagemaker_update_action]{update_action} \tab Updates an action\cr
#'  \link[=sagemaker_update_app_image_config]{update_app_image_config} \tab Updates the properties of an AppImageConfig\cr
#'  \link[=sagemaker_update_artifact]{update_artifact} \tab Updates an artifact\cr
#'  \link[=sagemaker_update_cluster]{update_cluster} \tab Updates a SageMaker HyperPod cluster\cr
#'  \link[=sagemaker_update_cluster_scheduler_config]{update_cluster_scheduler_config} \tab Update the cluster policy configuration\cr
#'  \link[=sagemaker_update_cluster_software]{update_cluster_software} \tab Updates the platform software of a SageMaker HyperPod cluster for security patching\cr
#'  \link[=sagemaker_update_code_repository]{update_code_repository} \tab Updates the specified Git repository with the specified values\cr
#'  \link[=sagemaker_update_compute_quota]{update_compute_quota} \tab Update the compute allocation definition\cr
#'  \link[=sagemaker_update_context]{update_context} \tab Updates a context\cr
#'  \link[=sagemaker_update_device_fleet]{update_device_fleet} \tab Updates a fleet of devices\cr
#'  \link[=sagemaker_update_devices]{update_devices} \tab Updates one or more devices in a fleet\cr
#'  \link[=sagemaker_update_domain]{update_domain} \tab Updates the default settings for new user profiles in the domain\cr
#'  \link[=sagemaker_update_endpoint]{update_endpoint} \tab Deploys the EndpointConfig specified in the request to a new fleet of instances\cr
#'  \link[=sagemaker_update_endpoint_weights_and_capacities]{update_endpoint_weights_and_capacities} \tab Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint\cr
#'  \link[=sagemaker_update_experiment]{update_experiment} \tab Adds, updates, or removes the description of an experiment\cr
#'  \link[=sagemaker_update_feature_group]{update_feature_group} \tab Updates the feature group by either adding features or updating the online store configuration\cr
#'  \link[=sagemaker_update_feature_metadata]{update_feature_metadata} \tab Updates the description and parameters of the feature group\cr
#'  \link[=sagemaker_update_hub]{update_hub} \tab Update a hub\cr
#'  \link[=sagemaker_update_image]{update_image} \tab Updates the properties of a SageMaker AI image\cr
#'  \link[=sagemaker_update_image_version]{update_image_version} \tab Updates the properties of a SageMaker AI image version\cr
#'  \link[=sagemaker_update_inference_component]{update_inference_component} \tab Updates an inference component\cr
#'  \link[=sagemaker_update_inference_component_runtime_config]{update_inference_component_runtime_config} \tab Runtime settings for a model that is deployed with an inference component\cr
#'  \link[=sagemaker_update_inference_experiment]{update_inference_experiment} \tab Updates an inference experiment that you created\cr
#'  \link[=sagemaker_update_mlflow_tracking_server]{update_mlflow_tracking_server} \tab Updates properties of an existing MLflow Tracking Server\cr
#'  \link[=sagemaker_update_model_card]{update_model_card} \tab Update an Amazon SageMaker Model Card\cr
#'  \link[=sagemaker_update_model_package]{update_model_package} \tab Updates a versioned model\cr
#'  \link[=sagemaker_update_monitoring_alert]{update_monitoring_alert} \tab Update the parameters of a model monitor alert\cr
#'  \link[=sagemaker_update_monitoring_schedule]{update_monitoring_schedule} \tab Updates a previously created schedule\cr
#'  \link[=sagemaker_update_notebook_instance]{update_notebook_instance} \tab Updates a notebook instance\cr
#'  \link[=sagemaker_update_notebook_instance_lifecycle_config]{update_notebook_instance_lifecycle_config} \tab Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API\cr
#'  \link[=sagemaker_update_partner_app]{update_partner_app} \tab Updates all of the SageMaker Partner AI Apps in an account\cr
#'  \link[=sagemaker_update_pipeline]{update_pipeline} \tab Updates a pipeline\cr
#'  \link[=sagemaker_update_pipeline_execution]{update_pipeline_execution} \tab Updates a pipeline execution\cr
#'  \link[=sagemaker_update_project]{update_project} \tab Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model\cr
#'  \link[=sagemaker_update_space]{update_space} \tab Updates the settings of a space\cr
#'  \link[=sagemaker_update_training_job]{update_training_job} \tab Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length\cr
#'  \link[=sagemaker_update_trial]{update_trial} \tab Updates the display name of a trial\cr
#'  \link[=sagemaker_update_trial_component]{update_trial_component} \tab Updates one or more properties of a trial component\cr
#'  \link[=sagemaker_update_user_profile]{update_user_profile} \tab Updates a user profile\cr
#'  \link[=sagemaker_update_workforce]{update_workforce} \tab Use this operation to update your workforce\cr
#'  \link[=sagemaker_update_workteam]{update_workteam} \tab Updates an existing work team with new member definitions or description
#' }
#'
#' @return
#' A client for the service. You can call the service's operations using
#' syntax like `svc$operation(...)`, where `svc` is the name you've assigned
#' to the client. The available operations are listed in the
#' Operations section.
#'
#' @rdname sagemaker
#' @export
sagemaker <- function(config = list(), credentials = list(), endpoint = NULL, region = NULL) {
  config <- merge_config(
    config,
    list(
      credentials = credentials,
      endpoint = endpoint,
      region = region
    )
  )
  svc <- .sagemaker$operations
  svc <- set_config(svc, config)
  return(svc)
}

# Private API objects: metadata, handlers, interfaces, etc.
.sagemaker <- list()

.sagemaker$operations <- list()

.sagemaker$metadata <- list(
  service_name = "sagemaker",
  endpoints = list("^(us|eu|ap|sa|ca|me|af|il|mx)\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.amazonaws.com", global = FALSE), "^cn\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.amazonaws.com.cn", global = FALSE), "^us\\-gov\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.amazonaws.com", global = FALSE), "^us\\-iso\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.c2s.ic.gov", global = FALSE), "^us\\-isob\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.sc2s.sgov.gov", global = FALSE), "^eu\\-isoe\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.cloud.adc-e.uk", global = FALSE), "^us\\-isof\\-\\w+\\-\\d+$" = list(endpoint = "api.sagemaker.{region}.csp.hci.ic.gov", global = FALSE)),
  service_id = "SageMaker",
  api_version = "2017-07-24",
  signing_name = "sagemaker",
  json_version = "1.1",
  target_prefix = "SageMaker"
)

.sagemaker$service <- function(config = list(), op = NULL) {
  handlers <- new_handlers("jsonrpc", "v4")
  new_service(.sagemaker$metadata, handlers, config, op)
}