sagemaker: Amazon SageMaker Service
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services
View source: R/sagemaker_service.R
sagemaker R Documentation
Amazon SageMaker Service
Description
Provides APIs for creating and managing SageMaker resources.
Other Resources:
Usage
sagemaker(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)
Arguments
config
Optional configuration of credentials, endpoint, and/or region.
credentials:
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
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.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html
credentials
Optional credentials shorthand for the config parameter
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
endpoint
Optional shorthand for complete URL to use for the constructed client.
region
Optional shorthand for AWS Region used in instantiating the client.
Value
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.
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"
)
Operations
add_association Creates an association between the source and the destination
add_tags Adds or overwrites one or more tags for the specified SageMaker resource
associate_trial_component Associates a trial component with a trial
batch_describe_model_package This action batch describes a list of versioned model packages
create_action Creates an action
create_algorithm Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace
create_app Creates a running app for the specified UserProfile
create_app_image_config Creates a configuration for running a SageMaker image as a KernelGateway app
create_artifact Creates an artifact
create_auto_ml_job Creates an Autopilot job also referred to as Autopilot experiment or AutoML job
create_auto_ml_job_v2 Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2
create_code_repository Creates a Git repository as a resource in your SageMaker account
create_compilation_job Starts a model compilation job
create_context Creates a context
create_data_quality_job_definition Creates a definition for a job that monitors data quality and drift
create_device_fleet Creates a device fleet
create_domain Creates a Domain used by Amazon SageMaker Studio
create_edge_deployment_plan Creates an edge deployment plan, consisting of multiple stages
create_edge_deployment_stage Creates a new stage in an existing edge deployment plan
create_edge_packaging_job Starts a SageMaker Edge Manager model packaging job
create_endpoint Creates an endpoint using the endpoint configuration specified in the request
create_endpoint_config Creates an endpoint configuration that SageMaker hosting services uses to deploy models
create_experiment Creates a SageMaker experiment
create_feature_group Create a new FeatureGroup
create_flow_definition Creates a flow definition
create_hub Create a hub
create_human_task_ui Defines the settings you will use for the human review workflow user interface
create_hyper_parameter_tuning_job Starts a hyperparameter tuning job
create_image Creates a custom SageMaker image
create_image_version Creates a version of the SageMaker image specified by ImageName
create_inference_experiment Creates an inference experiment using the configurations specified in the request
create_inference_recommendations_job Starts a recommendation job
create_labeling_job Creates a job that uses workers to label the data objects in your input dataset
create_model Creates a model in SageMaker
create_model_bias_job_definition Creates the definition for a model bias job
create_model_card Creates an Amazon SageMaker Model Card
create_model_card_export_job Creates an Amazon SageMaker Model Card export job
create_model_explainability_job_definition Creates the definition for a model explainability job
create_model_package 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
create_model_package_group Creates a model group
create_model_quality_job_definition Creates a definition for a job that monitors model quality and drift
create_monitoring_schedule Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint
create_notebook_instance Creates an SageMaker notebook instance
create_notebook_instance_lifecycle_config Creates a lifecycle configuration that you can associate with a notebook instance
create_pipeline Creates a pipeline using a JSON pipeline definition
create_presigned_domain_url Creates a URL for a specified UserProfile in a Domain
create_presigned_notebook_instance_url Returns a URL that you can use to connect to the Jupyter server from a notebook instance
create_processing_job Creates a processing job
create_project 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
create_space Creates a space used for real time collaboration in a Domain
create_studio_lifecycle_config Creates a new Studio Lifecycle Configuration
create_training_job Starts a model training job
create_transform_job Starts a transform job
create_trial Creates an SageMaker trial
create_trial_component Creates a trial component, which is a stage of a machine learning trial
create_user_profile Creates a user profile
create_workforce Use this operation to create a workforce
create_workteam Creates a new work team for labeling your data
delete_action Deletes an action
delete_algorithm Removes the specified algorithm from your account
delete_app Used to stop and delete an app
delete_app_image_config Deletes an AppImageConfig
delete_artifact Deletes an artifact
delete_association Deletes an association
delete_code_repository Deletes the specified Git repository from your account
delete_context Deletes an context
delete_data_quality_job_definition Deletes a data quality monitoring job definition
delete_device_fleet Deletes a fleet
delete_domain Used to delete a domain
delete_edge_deployment_plan 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
delete_edge_deployment_stage Delete a stage in an edge deployment plan if (and only if) the stage is inactive
delete_endpoint Deletes an endpoint
delete_endpoint_config Deletes an endpoint configuration
delete_experiment Deletes an SageMaker experiment
delete_feature_group Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup
delete_flow_definition Deletes the specified flow definition
delete_hub Delete a hub
delete_hub_content Delete the contents of a hub
delete_human_task_ui Use this operation to delete a human task user interface (worker task template)
delete_image Deletes a SageMaker image and all versions of the image
delete_image_version Deletes a version of a SageMaker image
delete_inference_experiment Deletes an inference experiment
delete_model Deletes a model
delete_model_bias_job_definition Deletes an Amazon SageMaker model bias job definition
delete_model_card Deletes an Amazon SageMaker Model Card
delete_model_explainability_job_definition Deletes an Amazon SageMaker model explainability job definition
delete_model_package Deletes a model package
delete_model_package_group Deletes the specified model group
delete_model_package_group_policy Deletes a model group resource policy
delete_model_quality_job_definition Deletes the secified model quality monitoring job definition
delete_monitoring_schedule Deletes a monitoring schedule
delete_notebook_instance Deletes an SageMaker notebook instance
delete_notebook_instance_lifecycle_config Deletes a notebook instance lifecycle configuration
delete_pipeline Deletes a pipeline if there are no running instances of the pipeline
delete_project Delete the specified project
delete_space Used to delete a space
delete_studio_lifecycle_config Deletes the Studio Lifecycle Configuration
delete_tags Deletes the specified tags from an SageMaker resource
delete_trial Deletes the specified trial
delete_trial_component Deletes the specified trial component
delete_user_profile Deletes a user profile
delete_workforce Use this operation to delete a workforce
delete_workteam Deletes an existing work team
deregister_devices Deregisters the specified devices
describe_action Describes an action
describe_algorithm Returns a description of the specified algorithm that is in your account
describe_app Describes the app
describe_app_image_config Describes an AppImageConfig
describe_artifact Describes an artifact
describe_auto_ml_job Returns information about an AutoML job created by calling CreateAutoMLJob
describe_auto_ml_job_v2 Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob
describe_code_repository Gets details about the specified Git repository
describe_compilation_job Returns information about a model compilation job
describe_context Describes a context
describe_data_quality_job_definition Gets the details of a data quality monitoring job definition
describe_device Describes the device
describe_device_fleet A description of the fleet the device belongs to
describe_domain The description of the domain
describe_edge_deployment_plan Describes an edge deployment plan with deployment status per stage
describe_edge_packaging_job A description of edge packaging jobs
describe_endpoint Returns the description of an endpoint
describe_endpoint_config Returns the description of an endpoint configuration created using the CreateEndpointConfig API
describe_experiment Provides a list of an experiment's properties
describe_feature_group Use this operation to describe a FeatureGroup
describe_feature_metadata Shows the metadata for a feature within a feature group
describe_flow_definition Returns information about the specified flow definition
describe_hub Describe a hub
describe_hub_content Describe the content of a hub
describe_human_task_ui Returns information about the requested human task user interface (worker task template)
describe_hyper_parameter_tuning_job Returns a description of a hyperparameter tuning job, depending on the fields selected
describe_image Describes a SageMaker image
describe_image_version Describes a version of a SageMaker image
describe_inference_experiment Returns details about an inference experiment
describe_inference_recommendations_job Provides the results of the Inference Recommender job
describe_labeling_job Gets information about a labeling job
describe_lineage_group Provides a list of properties for the requested lineage group
describe_model Describes a model that you created using the CreateModel API
describe_model_bias_job_definition Returns a description of a model bias job definition
describe_model_card Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card
describe_model_card_export_job Describes an Amazon SageMaker Model Card export job
describe_model_explainability_job_definition Returns a description of a model explainability job definition
describe_model_package Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace
describe_model_package_group Gets a description for the specified model group
describe_model_quality_job_definition Returns a description of a model quality job definition
describe_monitoring_schedule Describes the schedule for a monitoring job
describe_notebook_instance Returns information about a notebook instance
describe_notebook_instance_lifecycle_config Returns a description of a notebook instance lifecycle configuration
describe_pipeline Describes the details of a pipeline
describe_pipeline_definition_for_execution Describes the details of an execution's pipeline definition
describe_pipeline_execution Describes the details of a pipeline execution
describe_processing_job Returns a description of a processing job
describe_project Describes the details of a project
describe_space Describes the space
describe_studio_lifecycle_config Describes the Studio Lifecycle Configuration
describe_subscribed_workteam Gets information about a work team provided by a vendor
describe_training_job Returns information about a training job
describe_transform_job Returns information about a transform job
describe_trial Provides a list of a trial's properties
describe_trial_component Provides a list of a trials component's properties
describe_user_profile Describes a user profile
describe_workforce Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs)
describe_workteam Gets information about a specific work team
disable_sagemaker_servicecatalog_portfolio Disables using Service Catalog in SageMaker
disassociate_trial_component Disassociates a trial component from a trial
enable_sagemaker_servicecatalog_portfolio Enables using Service Catalog in SageMaker
get_device_fleet_report Describes a fleet
get_lineage_group_policy The resource policy for the lineage group
get_model_package_group_policy Gets a resource policy that manages access for a model group
get_sagemaker_servicecatalog_portfolio_status Gets the status of Service Catalog in SageMaker
get_scaling_configuration_recommendation Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job
get_search_suggestions An auto-complete API for the search functionality in the SageMaker console
import_hub_content Import hub content
list_actions Lists the actions in your account and their properties
list_algorithms Lists the machine learning algorithms that have been created
list_aliases Lists the aliases of a specified image or image version
list_app_image_configs Lists the AppImageConfigs in your account and their properties
list_apps Lists apps
list_artifacts Lists the artifacts in your account and their properties
list_associations Lists the associations in your account and their properties
list_auto_ml_jobs Request a list of jobs
list_candidates_for_auto_ml_job List the candidates created for the job
list_code_repositories Gets a list of the Git repositories in your account
list_compilation_jobs Lists model compilation jobs that satisfy various filters
list_contexts Lists the contexts in your account and their properties
list_data_quality_job_definitions Lists the data quality job definitions in your account
list_device_fleets Returns a list of devices in the fleet
list_devices A list of devices
list_domains Lists the domains
list_edge_deployment_plans Lists all edge deployment plans
list_edge_packaging_jobs Returns a list of edge packaging jobs
list_endpoint_configs Lists endpoint configurations
list_endpoints Lists endpoints
list_experiments Lists all the experiments in your account
list_feature_groups List FeatureGroups based on given filter and order
list_flow_definitions Returns information about the flow definitions in your account
list_hub_contents List the contents of a hub
list_hub_content_versions List hub content versions
list_hubs List all existing hubs
list_human_task_uis Returns information about the human task user interfaces in your account
list_hyper_parameter_tuning_jobs Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account
list_images Lists the images in your account and their properties
list_image_versions Lists the versions of a specified image and their properties
list_inference_experiments Returns the list of all inference experiments
list_inference_recommendations_jobs Lists recommendation jobs that satisfy various filters
list_inference_recommendations_job_steps Returns a list of the subtasks for an Inference Recommender job
list_labeling_jobs Gets a list of labeling jobs
list_labeling_jobs_for_workteam Gets a list of labeling jobs assigned to a specified work team
list_lineage_groups A list of lineage groups shared with your Amazon Web Services account
list_model_bias_job_definitions Lists model bias jobs definitions that satisfy various filters
list_model_card_export_jobs List the export jobs for the Amazon SageMaker Model Card
list_model_cards List existing model cards
list_model_card_versions List existing versions of an Amazon SageMaker Model Card
list_model_explainability_job_definitions Lists model explainability job definitions that satisfy various filters
list_model_metadata Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos
list_model_package_groups Gets a list of the model groups in your Amazon Web Services account
list_model_packages Lists the model packages that have been created
list_model_quality_job_definitions Gets a list of model quality monitoring job definitions in your account
list_models Lists models created with the CreateModel API
list_monitoring_alert_history Gets a list of past alerts in a model monitoring schedule
list_monitoring_alerts Gets the alerts for a single monitoring schedule
list_monitoring_executions Returns list of all monitoring job executions
list_monitoring_schedules Returns list of all monitoring schedules
list_notebook_instance_lifecycle_configs Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API
list_notebook_instances Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region
list_pipeline_executions Gets a list of the pipeline executions
list_pipeline_execution_steps Gets a list of PipeLineExecutionStep objects
list_pipeline_parameters_for_execution Gets a list of parameters for a pipeline execution
list_pipelines Gets a list of pipelines
list_processing_jobs Lists processing jobs that satisfy various filters
list_projects Gets a list of the projects in an Amazon Web Services account
list_resource_catalogs Lists Amazon SageMaker Catalogs based on given filters and orders
list_spaces Lists spaces
list_stage_devices Lists devices allocated to the stage, containing detailed device information and deployment status
list_studio_lifecycle_configs Lists the Studio Lifecycle Configurations in your Amazon Web Services Account
list_subscribed_workteams Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace
list_tags Returns the tags for the specified SageMaker resource
list_training_jobs Lists training jobs
list_training_jobs_for_hyper_parameter_tuning_job Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched
list_transform_jobs Lists transform jobs
list_trial_components Lists the trial components in your account
list_trials Lists the trials in your account
list_user_profiles Lists user profiles
list_workforces Use this operation to list all private and vendor workforces in an Amazon Web Services Region
list_workteams Gets a list of private work teams that you have defined in a region
put_model_package_group_policy Adds a resouce policy to control access to a model group
query_lineage Use this action to inspect your lineage and discover relationships between entities
register_devices Register devices
render_ui_template Renders the UI template so that you can preview the worker's experience
retry_pipeline_execution Retry the execution of the pipeline
search Finds SageMaker resources that match a search query
send_pipeline_execution_step_failure Notifies the pipeline that the execution of a callback step failed, along with a message describing why
send_pipeline_execution_step_success Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters
start_edge_deployment_stage Starts a stage in an edge deployment plan
start_inference_experiment Starts an inference experiment
start_monitoring_schedule Starts a previously stopped monitoring schedule
start_notebook_instance Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume
start_pipeline_execution Starts a pipeline execution
stop_auto_ml_job A method for forcing a running job to shut down
stop_compilation_job Stops a model compilation job
stop_edge_deployment_stage Stops a stage in an edge deployment plan
stop_edge_packaging_job Request to stop an edge packaging job
stop_hyper_parameter_tuning_job Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched
stop_inference_experiment Stops an inference experiment
stop_inference_recommendations_job Stops an Inference Recommender job
stop_labeling_job Stops a running labeling job
stop_monitoring_schedule Stops a previously started monitoring schedule
stop_notebook_instance Terminates the ML compute instance
stop_pipeline_execution Stops a pipeline execution
stop_processing_job Stops a processing job
stop_training_job Stops a training job
stop_transform_job Stops a batch transform job
update_action Updates an action
update_app_image_config Updates the properties of an AppImageConfig
update_artifact Updates an artifact
update_code_repository Updates the specified Git repository with the specified values
update_context Updates a context
update_device_fleet Updates a fleet of devices
update_devices Updates one or more devices in a fleet
update_domain Updates the default settings for new user profiles in the domain
update_endpoint Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss)
update_endpoint_weights_and_capacities Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint
update_experiment Adds, updates, or removes the description of an experiment
update_feature_group Updates the feature group by either adding features or updating the online store configuration
update_feature_metadata Updates the description and parameters of the feature group
update_hub Update a hub
update_image Updates the properties of a SageMaker image
update_image_version Updates the properties of a SageMaker image version
update_inference_experiment Updates an inference experiment that you created
update_model_card Update an Amazon SageMaker Model Card
update_model_package Updates a versioned model
update_monitoring_alert Update the parameters of a model monitor alert
update_monitoring_schedule Updates a previously created schedule
update_notebook_instance Updates a notebook instance
update_notebook_instance_lifecycle_config Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API
update_pipeline Updates a pipeline
update_pipeline_execution Updates a pipeline execution
update_project 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
update_space Updates the settings of a space
update_training_job Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length
update_trial Updates the display name of a trial
update_trial_component Updates one or more properties of a trial component
update_user_profile Updates a user profile
update_workforce Use this operation to update your workforce
update_workteam Updates an existing work team with new member definitions or description
Examples
## Not run:
svc <- sagemaker()
svc$add_association(
Foo = 123
)
## End(Not run)
paws.machine.learning documentation built on Sept. 12, 2023, 1:14 a.m.
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View source: R/sagemaker_service.R
| sagemaker | R Documentation |
Amazon SageMaker Service
Description
Provides APIs for creating and managing SageMaker resources.
Other Resources:
Usage
sagemaker(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)
Arguments
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
Value
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.
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"
)
Operations
| add_association | Creates an association between the source and the destination |
| add_tags | Adds or overwrites one or more tags for the specified SageMaker resource |
| associate_trial_component | Associates a trial component with a trial |
| batch_describe_model_package | This action batch describes a list of versioned model packages |
| create_action | Creates an action |
| create_algorithm | Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace |
| create_app | Creates a running app for the specified UserProfile |
| create_app_image_config | Creates a configuration for running a SageMaker image as a KernelGateway app |
| create_artifact | Creates an artifact |
| create_auto_ml_job | Creates an Autopilot job also referred to as Autopilot experiment or AutoML job |
| create_auto_ml_job_v2 | Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2 |
| create_code_repository | Creates a Git repository as a resource in your SageMaker account |
| create_compilation_job | Starts a model compilation job |
| create_context | Creates a context |
| create_data_quality_job_definition | Creates a definition for a job that monitors data quality and drift |
| create_device_fleet | Creates a device fleet |
| create_domain | Creates a Domain used by Amazon SageMaker Studio |
| create_edge_deployment_plan | Creates an edge deployment plan, consisting of multiple stages |
| create_edge_deployment_stage | Creates a new stage in an existing edge deployment plan |
| create_edge_packaging_job | Starts a SageMaker Edge Manager model packaging job |
| create_endpoint | Creates an endpoint using the endpoint configuration specified in the request |
| create_endpoint_config | Creates an endpoint configuration that SageMaker hosting services uses to deploy models |
| create_experiment | Creates a SageMaker experiment |
| create_feature_group | Create a new FeatureGroup |
| create_flow_definition | Creates a flow definition |
| create_hub | Create a hub |
| create_human_task_ui | Defines the settings you will use for the human review workflow user interface |
| create_hyper_parameter_tuning_job | Starts a hyperparameter tuning job |
| create_image | Creates a custom SageMaker image |
| create_image_version | Creates a version of the SageMaker image specified by ImageName |
| create_inference_experiment | Creates an inference experiment using the configurations specified in the request |
| create_inference_recommendations_job | Starts a recommendation job |
| create_labeling_job | Creates a job that uses workers to label the data objects in your input dataset |
| create_model | Creates a model in SageMaker |
| create_model_bias_job_definition | Creates the definition for a model bias job |
| create_model_card | Creates an Amazon SageMaker Model Card |
| create_model_card_export_job | Creates an Amazon SageMaker Model Card export job |
| create_model_explainability_job_definition | Creates the definition for a model explainability job |
| create_model_package | 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 |
| create_model_package_group | Creates a model group |
| create_model_quality_job_definition | Creates a definition for a job that monitors model quality and drift |
| create_monitoring_schedule | Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint |
| create_notebook_instance | Creates an SageMaker notebook instance |
| create_notebook_instance_lifecycle_config | Creates a lifecycle configuration that you can associate with a notebook instance |
| create_pipeline | Creates a pipeline using a JSON pipeline definition |
| create_presigned_domain_url | Creates a URL for a specified UserProfile in a Domain |
| create_presigned_notebook_instance_url | Returns a URL that you can use to connect to the Jupyter server from a notebook instance |
| create_processing_job | Creates a processing job |
| create_project | 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 |
| create_space | Creates a space used for real time collaboration in a Domain |
| create_studio_lifecycle_config | Creates a new Studio Lifecycle Configuration |
| create_training_job | Starts a model training job |
| create_transform_job | Starts a transform job |
| create_trial | Creates an SageMaker trial |
| create_trial_component | Creates a trial component, which is a stage of a machine learning trial |
| create_user_profile | Creates a user profile |
| create_workforce | Use this operation to create a workforce |
| create_workteam | Creates a new work team for labeling your data |
| delete_action | Deletes an action |
| delete_algorithm | Removes the specified algorithm from your account |
| delete_app | Used to stop and delete an app |
| delete_app_image_config | Deletes an AppImageConfig |
| delete_artifact | Deletes an artifact |
| delete_association | Deletes an association |
| delete_code_repository | Deletes the specified Git repository from your account |
| delete_context | Deletes an context |
| delete_data_quality_job_definition | Deletes a data quality monitoring job definition |
| delete_device_fleet | Deletes a fleet |
| delete_domain | Used to delete a domain |
| delete_edge_deployment_plan | 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 |
| delete_edge_deployment_stage | Delete a stage in an edge deployment plan if (and only if) the stage is inactive |
| delete_endpoint | Deletes an endpoint |
| delete_endpoint_config | Deletes an endpoint configuration |
| delete_experiment | Deletes an SageMaker experiment |
| delete_feature_group | Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup |
| delete_flow_definition | Deletes the specified flow definition |
| delete_hub | Delete a hub |
| delete_hub_content | Delete the contents of a hub |
| delete_human_task_ui | Use this operation to delete a human task user interface (worker task template) |
| delete_image | Deletes a SageMaker image and all versions of the image |
| delete_image_version | Deletes a version of a SageMaker image |
| delete_inference_experiment | Deletes an inference experiment |
| delete_model | Deletes a model |
| delete_model_bias_job_definition | Deletes an Amazon SageMaker model bias job definition |
| delete_model_card | Deletes an Amazon SageMaker Model Card |
| delete_model_explainability_job_definition | Deletes an Amazon SageMaker model explainability job definition |
| delete_model_package | Deletes a model package |
| delete_model_package_group | Deletes the specified model group |
| delete_model_package_group_policy | Deletes a model group resource policy |
| delete_model_quality_job_definition | Deletes the secified model quality monitoring job definition |
| delete_monitoring_schedule | Deletes a monitoring schedule |
| delete_notebook_instance | Deletes an SageMaker notebook instance |
| delete_notebook_instance_lifecycle_config | Deletes a notebook instance lifecycle configuration |
| delete_pipeline | Deletes a pipeline if there are no running instances of the pipeline |
| delete_project | Delete the specified project |
| delete_space | Used to delete a space |
| delete_studio_lifecycle_config | Deletes the Studio Lifecycle Configuration |
| delete_tags | Deletes the specified tags from an SageMaker resource |
| delete_trial | Deletes the specified trial |
| delete_trial_component | Deletes the specified trial component |
| delete_user_profile | Deletes a user profile |
| delete_workforce | Use this operation to delete a workforce |
| delete_workteam | Deletes an existing work team |
| deregister_devices | Deregisters the specified devices |
| describe_action | Describes an action |
| describe_algorithm | Returns a description of the specified algorithm that is in your account |
| describe_app | Describes the app |
| describe_app_image_config | Describes an AppImageConfig |
| describe_artifact | Describes an artifact |
| describe_auto_ml_job | Returns information about an AutoML job created by calling CreateAutoMLJob |
| describe_auto_ml_job_v2 | Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob |
| describe_code_repository | Gets details about the specified Git repository |
| describe_compilation_job | Returns information about a model compilation job |
| describe_context | Describes a context |
| describe_data_quality_job_definition | Gets the details of a data quality monitoring job definition |
| describe_device | Describes the device |
| describe_device_fleet | A description of the fleet the device belongs to |
| describe_domain | The description of the domain |
| describe_edge_deployment_plan | Describes an edge deployment plan with deployment status per stage |
| describe_edge_packaging_job | A description of edge packaging jobs |
| describe_endpoint | Returns the description of an endpoint |
| describe_endpoint_config | Returns the description of an endpoint configuration created using the CreateEndpointConfig API |
| describe_experiment | Provides a list of an experiment's properties |
| describe_feature_group | Use this operation to describe a FeatureGroup |
| describe_feature_metadata | Shows the metadata for a feature within a feature group |
| describe_flow_definition | Returns information about the specified flow definition |
| describe_hub | Describe a hub |
| describe_hub_content | Describe the content of a hub |
| describe_human_task_ui | Returns information about the requested human task user interface (worker task template) |
| describe_hyper_parameter_tuning_job | Returns a description of a hyperparameter tuning job, depending on the fields selected |
| describe_image | Describes a SageMaker image |
| describe_image_version | Describes a version of a SageMaker image |
| describe_inference_experiment | Returns details about an inference experiment |
| describe_inference_recommendations_job | Provides the results of the Inference Recommender job |
| describe_labeling_job | Gets information about a labeling job |
| describe_lineage_group | Provides a list of properties for the requested lineage group |
| describe_model | Describes a model that you created using the CreateModel API |
| describe_model_bias_job_definition | Returns a description of a model bias job definition |
| describe_model_card | Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card |
| describe_model_card_export_job | Describes an Amazon SageMaker Model Card export job |
| describe_model_explainability_job_definition | Returns a description of a model explainability job definition |
| describe_model_package | Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace |
| describe_model_package_group | Gets a description for the specified model group |
| describe_model_quality_job_definition | Returns a description of a model quality job definition |
| describe_monitoring_schedule | Describes the schedule for a monitoring job |
| describe_notebook_instance | Returns information about a notebook instance |
| describe_notebook_instance_lifecycle_config | Returns a description of a notebook instance lifecycle configuration |
| describe_pipeline | Describes the details of a pipeline |
| describe_pipeline_definition_for_execution | Describes the details of an execution's pipeline definition |
| describe_pipeline_execution | Describes the details of a pipeline execution |
| describe_processing_job | Returns a description of a processing job |
| describe_project | Describes the details of a project |
| describe_space | Describes the space |
| describe_studio_lifecycle_config | Describes the Studio Lifecycle Configuration |
| describe_subscribed_workteam | Gets information about a work team provided by a vendor |
| describe_training_job | Returns information about a training job |
| describe_transform_job | Returns information about a transform job |
| describe_trial | Provides a list of a trial's properties |
| describe_trial_component | Provides a list of a trials component's properties |
| describe_user_profile | Describes a user profile |
| describe_workforce | Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs) |
| describe_workteam | Gets information about a specific work team |
| disable_sagemaker_servicecatalog_portfolio | Disables using Service Catalog in SageMaker |
| disassociate_trial_component | Disassociates a trial component from a trial |
| enable_sagemaker_servicecatalog_portfolio | Enables using Service Catalog in SageMaker |
| get_device_fleet_report | Describes a fleet |
| get_lineage_group_policy | The resource policy for the lineage group |
| get_model_package_group_policy | Gets a resource policy that manages access for a model group |
| get_sagemaker_servicecatalog_portfolio_status | Gets the status of Service Catalog in SageMaker |
| get_scaling_configuration_recommendation | Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job |
| get_search_suggestions | An auto-complete API for the search functionality in the SageMaker console |
| import_hub_content | Import hub content |
| list_actions | Lists the actions in your account and their properties |
| list_algorithms | Lists the machine learning algorithms that have been created |
| list_aliases | Lists the aliases of a specified image or image version |
| list_app_image_configs | Lists the AppImageConfigs in your account and their properties |
| list_apps | Lists apps |
| list_artifacts | Lists the artifacts in your account and their properties |
| list_associations | Lists the associations in your account and their properties |
| list_auto_ml_jobs | Request a list of jobs |
| list_candidates_for_auto_ml_job | List the candidates created for the job |
| list_code_repositories | Gets a list of the Git repositories in your account |
| list_compilation_jobs | Lists model compilation jobs that satisfy various filters |
| list_contexts | Lists the contexts in your account and their properties |
| list_data_quality_job_definitions | Lists the data quality job definitions in your account |
| list_device_fleets | Returns a list of devices in the fleet |
| list_devices | A list of devices |
| list_domains | Lists the domains |
| list_edge_deployment_plans | Lists all edge deployment plans |
| list_edge_packaging_jobs | Returns a list of edge packaging jobs |
| list_endpoint_configs | Lists endpoint configurations |
| list_endpoints | Lists endpoints |
| list_experiments | Lists all the experiments in your account |
| list_feature_groups | List FeatureGroups based on given filter and order |
| list_flow_definitions | Returns information about the flow definitions in your account |
| list_hub_contents | List the contents of a hub |
| list_hub_content_versions | List hub content versions |
| list_hubs | List all existing hubs |
| list_human_task_uis | Returns information about the human task user interfaces in your account |
| list_hyper_parameter_tuning_jobs | Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account |
| list_images | Lists the images in your account and their properties |
| list_image_versions | Lists the versions of a specified image and their properties |
| list_inference_experiments | Returns the list of all inference experiments |
| list_inference_recommendations_jobs | Lists recommendation jobs that satisfy various filters |
| list_inference_recommendations_job_steps | Returns a list of the subtasks for an Inference Recommender job |
| list_labeling_jobs | Gets a list of labeling jobs |
| list_labeling_jobs_for_workteam | Gets a list of labeling jobs assigned to a specified work team |
| list_lineage_groups | A list of lineage groups shared with your Amazon Web Services account |
| list_model_bias_job_definitions | Lists model bias jobs definitions that satisfy various filters |
| list_model_card_export_jobs | List the export jobs for the Amazon SageMaker Model Card |
| list_model_cards | List existing model cards |
| list_model_card_versions | List existing versions of an Amazon SageMaker Model Card |
| list_model_explainability_job_definitions | Lists model explainability job definitions that satisfy various filters |
| list_model_metadata | Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos |
| list_model_package_groups | Gets a list of the model groups in your Amazon Web Services account |
| list_model_packages | Lists the model packages that have been created |
| list_model_quality_job_definitions | Gets a list of model quality monitoring job definitions in your account |
| list_models | Lists models created with the CreateModel API |
| list_monitoring_alert_history | Gets a list of past alerts in a model monitoring schedule |
| list_monitoring_alerts | Gets the alerts for a single monitoring schedule |
| list_monitoring_executions | Returns list of all monitoring job executions |
| list_monitoring_schedules | Returns list of all monitoring schedules |
| list_notebook_instance_lifecycle_configs | Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API |
| list_notebook_instances | Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region |
| list_pipeline_executions | Gets a list of the pipeline executions |
| list_pipeline_execution_steps | Gets a list of PipeLineExecutionStep objects |
| list_pipeline_parameters_for_execution | Gets a list of parameters for a pipeline execution |
| list_pipelines | Gets a list of pipelines |
| list_processing_jobs | Lists processing jobs that satisfy various filters |
| list_projects | Gets a list of the projects in an Amazon Web Services account |
| list_resource_catalogs | Lists Amazon SageMaker Catalogs based on given filters and orders |
| list_spaces | Lists spaces |
| list_stage_devices | Lists devices allocated to the stage, containing detailed device information and deployment status |
| list_studio_lifecycle_configs | Lists the Studio Lifecycle Configurations in your Amazon Web Services Account |
| list_subscribed_workteams | Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace |
| list_tags | Returns the tags for the specified SageMaker resource |
| list_training_jobs | Lists training jobs |
| list_training_jobs_for_hyper_parameter_tuning_job | Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched |
| list_transform_jobs | Lists transform jobs |
| list_trial_components | Lists the trial components in your account |
| list_trials | Lists the trials in your account |
| list_user_profiles | Lists user profiles |
| list_workforces | Use this operation to list all private and vendor workforces in an Amazon Web Services Region |
| list_workteams | Gets a list of private work teams that you have defined in a region |
| put_model_package_group_policy | Adds a resouce policy to control access to a model group |
| query_lineage | Use this action to inspect your lineage and discover relationships between entities |
| register_devices | Register devices |
| render_ui_template | Renders the UI template so that you can preview the worker's experience |
| retry_pipeline_execution | Retry the execution of the pipeline |
| search | Finds SageMaker resources that match a search query |
| send_pipeline_execution_step_failure | Notifies the pipeline that the execution of a callback step failed, along with a message describing why |
| send_pipeline_execution_step_success | Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters |
| start_edge_deployment_stage | Starts a stage in an edge deployment plan |
| start_inference_experiment | Starts an inference experiment |
| start_monitoring_schedule | Starts a previously stopped monitoring schedule |
| start_notebook_instance | Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume |
| start_pipeline_execution | Starts a pipeline execution |
| stop_auto_ml_job | A method for forcing a running job to shut down |
| stop_compilation_job | Stops a model compilation job |
| stop_edge_deployment_stage | Stops a stage in an edge deployment plan |
| stop_edge_packaging_job | Request to stop an edge packaging job |
| stop_hyper_parameter_tuning_job | Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched |
| stop_inference_experiment | Stops an inference experiment |
| stop_inference_recommendations_job | Stops an Inference Recommender job |
| stop_labeling_job | Stops a running labeling job |
| stop_monitoring_schedule | Stops a previously started monitoring schedule |
| stop_notebook_instance | Terminates the ML compute instance |
| stop_pipeline_execution | Stops a pipeline execution |
| stop_processing_job | Stops a processing job |
| stop_training_job | Stops a training job |
| stop_transform_job | Stops a batch transform job |
| update_action | Updates an action |
| update_app_image_config | Updates the properties of an AppImageConfig |
| update_artifact | Updates an artifact |
| update_code_repository | Updates the specified Git repository with the specified values |
| update_context | Updates a context |
| update_device_fleet | Updates a fleet of devices |
| update_devices | Updates one or more devices in a fleet |
| update_domain | Updates the default settings for new user profiles in the domain |
| update_endpoint | Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss) |
| update_endpoint_weights_and_capacities | Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint |
| update_experiment | Adds, updates, or removes the description of an experiment |
| update_feature_group | Updates the feature group by either adding features or updating the online store configuration |
| update_feature_metadata | Updates the description and parameters of the feature group |
| update_hub | Update a hub |
| update_image | Updates the properties of a SageMaker image |
| update_image_version | Updates the properties of a SageMaker image version |
| update_inference_experiment | Updates an inference experiment that you created |
| update_model_card | Update an Amazon SageMaker Model Card |
| update_model_package | Updates a versioned model |
| update_monitoring_alert | Update the parameters of a model monitor alert |
| update_monitoring_schedule | Updates a previously created schedule |
| update_notebook_instance | Updates a notebook instance |
| update_notebook_instance_lifecycle_config | Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API |
| update_pipeline | Updates a pipeline |
| update_pipeline_execution | Updates a pipeline execution |
| update_project | 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 |
| update_space | Updates the settings of a space |
| update_training_job | Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length |
| update_trial | Updates the display name of a trial |
| update_trial_component | Updates one or more properties of a trial component |
| update_user_profile | Updates a user profile |
| update_workforce | Use this operation to update your workforce |
| update_workteam | Updates an existing work team with new member definitions or description |
Examples
## Not run:
svc <- sagemaker()
svc$add_association(
Foo = 123
)
## End(Not run)
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