Amazon SageMaker Service¶

Description¶

Provides APIs for creating and managing SageMaker resources.

Other Resources:

SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

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
- use_dual_stack: Set this to true to use the dualstack (IPv4 and IPv6) endpoint for a service, where available, falling back to the regular endpoint when it isn't. Defaults to the AWS_USE_DUALSTACK_ENDPOINT environment variable when unset.
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",
    use_dual_stack = "logical"
  ),
  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
`attach_cluster_node_network_interface`	Attaches an elastic network interface (ENI) to a node in a HyperPod cluster
`attach_cluster_node_volume`	Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster
`batch_add_cluster_nodes`	Adds nodes to a HyperPod cluster by incrementing the target count for one or more instance groups
`batch_delete_cluster_nodes`	Deletes specific nodes within a SageMaker HyperPod cluster
`batch_describe_model_package`	This action batch describes a list of versioned model packages
`batch_reboot_cluster_nodes`	Reboots specific nodes within a SageMaker HyperPod cluster using a soft recovery mechanism
`batch_replace_cluster_nodes`	Replaces specific nodes within a SageMaker HyperPod cluster with new hardware
`create_action`	Creates an action
`create_ai_benchmark_job`	Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration
`create_ai_recommendation_job`	Creates a recommendation job that generates intelligent optimization recommendations for generative AI inference deployments
`create_ai_workload_config`	Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI
`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 AI 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_cluster`	Creates an Amazon SageMaker HyperPod cluster
`create_cluster_scheduler_config`	Create cluster policy configuration
`create_code_repository`	Creates a Git repository as a resource in your SageMaker AI account
`create_compilation_job`	Starts a model compilation job
`create_compute_quota`	Create compute allocation definition
`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
`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_hub_content_presigned_urls`	Creates presigned URLs for accessing hub content artifacts
`create_hub_content_reference`	Create a hub content reference in order to add a model in the JumpStart public hub to a private 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 AI image
`create_image_version`	Creates a version of the SageMaker AI image specified by ImageName
`create_inference_component`	Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint
`create_inference_experiment`	Creates an inference experiment using the configurations specified in the request
`create_inference_recommendations_job`	Starts a recommendation job
`create_job`	Creates a model customization job in Amazon SageMaker
`create_labeling_job`	Creates a job that uses workers to label the data objects in your input dataset
`create_mlflow_app`	Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store
`create_mlflow_tracking_server`	Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store
`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 AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint
`create_notebook_instance`	Creates an SageMaker AI notebook instance
`create_notebook_instance_lifecycle_config`	Creates a lifecycle configuration that you can associate with a notebook instance
`create_optimization_job`	Creates a job that optimizes a model for inference performance
`create_partner_app`	Creates an Amazon SageMaker Partner AI App
`create_partner_app_presigned_url`	Creates a presigned URL to access an Amazon SageMaker Partner AI App
`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_mlflow_app_url`	Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App
`create_presigned_mlflow_tracking_server_url`	Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server
`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 private space or a space used for real time collaboration in a domain
`create_studio_lifecycle_config`	Creates a new Amazon SageMaker AI Studio Lifecycle Configuration
`create_training_job`	Starts a model training job
`create_training_plan`	Creates a new training plan in SageMaker to reserve compute capacity
`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_ai_benchmark_job`	Deletes the specified AI benchmark job
`delete_ai_recommendation_job`	Deletes the specified AI recommendation job
`delete_ai_workload_config`	Deletes the specified AI workload configuration
`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_cluster`	Delete a SageMaker HyperPod cluster
`delete_cluster_scheduler_config`	Deletes the cluster policy of the cluster
`delete_code_repository`	Deletes the specified Git repository from your account
`delete_compilation_job`	Deletes the specified compilation job
`delete_compute_quota`	Deletes the compute allocation from the cluster
`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_hub_content_reference`	Delete a hub content reference in order to remove a model from a private hub
`delete_human_task_ui`	Use this operation to delete a human task user interface (worker task template)
`delete_hyper_parameter_tuning_job`	Deletes a hyperparameter tuning job
`delete_image`	Deletes a SageMaker AI image and all versions of the image
`delete_image_version`	Deletes a version of a SageMaker AI image
`delete_inference_component`	Deletes an inference component
`delete_inference_experiment`	Deletes an inference experiment
`delete_job`	Deletes a job
`delete_mlflow_app`	Deletes an MLflow App
`delete_mlflow_tracking_server`	Deletes an MLflow Tracking Server
`delete_model`	Deletes a model
`delete_model_bias_job_definition`	Deletes an Amazon SageMaker AI model bias job definition
`delete_model_card`	Deletes an Amazon SageMaker Model Card
`delete_model_explainability_job_definition`	Deletes an Amazon SageMaker AI 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 AI notebook instance
`delete_notebook_instance_lifecycle_config`	Deletes a notebook instance lifecycle configuration
`delete_optimization_job`	Deletes an optimization job
`delete_partner_app`	Deletes a SageMaker Partner AI App
`delete_pipeline`	Deletes a pipeline if there are no running instances of the pipeline
`delete_processing_job`	Deletes a processing job
`delete_project`	Delete the specified project
`delete_space`	Used to delete a space
`delete_studio_lifecycle_config`	Deletes the Amazon SageMaker AI Studio Lifecycle Configuration
`delete_tags`	Deletes the specified tags from an SageMaker resource
`delete_training_job`	Deletes a training job
`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_ai_benchmark_job`	Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information
`describe_ai_recommendation_job`	Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations
`describe_ai_workload_config`	Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time
`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_cluster`	Retrieves information of a SageMaker HyperPod cluster
`describe_cluster_event`	Retrieves detailed information about a specific event for a given HyperPod cluster
`describe_cluster_node`	Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster
`describe_cluster_scheduler_config`	Description of the cluster policy
`describe_code_repository`	Gets details about the specified Git repository
`describe_compilation_job`	Returns information about a model compilation job
`describe_compute_quota`	Description of the compute allocation definition
`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`	Describes 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 AI image
`describe_image_version`	Describes a version of a SageMaker AI image
`describe_inference_component`	Returns information about an inference component
`describe_inference_experiment`	Returns details about an inference experiment
`describe_inference_recommendations_job`	Provides the results of the Inference Recommender job
`describe_job`	Returns detailed information about a job, including its current status, secondary status, configuration, and timestamps
`describe_job_schema_version`	Returns the JSON schema for a specified job category and schema version
`describe_labeling_job`	Gets information about a labeling job
`describe_lineage_group`	Provides a list of properties for the requested lineage group
`describe_mlflow_app`	Returns information about an MLflow App
`describe_mlflow_tracking_server`	Returns information about an MLflow Tracking Server
`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_optimization_job`	Provides the properties of the specified optimization job
`describe_partner_app`	Gets information about a SageMaker Partner AI App
`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_reserved_capacity`	Retrieves details about a reserved capacity
`describe_space`	Describes the space
`describe_studio_lifecycle_config`	Describes the Amazon SageMaker AI 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_training_plan`	Retrieves detailed information about a specific training plan
`describe_training_plan_extension_history`	Retrieves the extension history for a specified training plan
`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
`detach_cluster_node_volume`	Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster
`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
`extend_training_plan`	Extends an existing training plan by purchasing an extension offering
`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_ai_benchmark_jobs`	Returns a list of AI benchmark jobs in your account
`list_ai_recommendation_jobs`	Returns a list of AI recommendation jobs in your account
`list_ai_workload_configs`	Returns a list of AI workload configurations in your account
`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_cluster_events`	Retrieves a list of event summaries for a specified HyperPod cluster
`list_cluster_nodes`	Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster
`list_clusters`	Retrieves the list of SageMaker HyperPod clusters
`list_cluster_scheduler_configs`	List the cluster policy configurations
`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_compute_quotas`	List the resource allocation definitions
`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_components`	Lists the inference components in your account 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_jobs`	Lists jobs in a specified category
`list_job_schema_versions`	Lists available configuration schema versions for a specified job category
`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_mlflow_apps`	Lists all MLflow Apps
`list_mlflow_tracking_servers`	Lists all MLflow Tracking Servers
`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 AI notebook instances in the requester's account in an Amazon Web Services Region
`list_optimization_jobs`	Lists the optimization jobs in your account and their properties
`list_partner_apps`	Lists all of the SageMaker Partner AI Apps in an account
`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_pipeline_versions`	Gets a list of all versions of the pipeline
`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 Amazon SageMaker AI 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_training_plans`	Retrieves a list of training plans for the current account
`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_ultra_servers_by_reserved_capacity`	Lists all UltraServers that are part of a specified reserved capacity
`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
`search_training_plan_offerings`	Searches for available training plan offerings based on specified criteria
`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_cluster_health_check`	Start deep health checks for a SageMaker HyperPod cluster
`start_edge_deployment_stage`	Starts a stage in an edge deployment plan
`start_inference_experiment`	Starts an inference experiment
`start_mlflow_tracking_server`	Programmatically start an MLflow Tracking Server
`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
`start_session`	Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space
`stop_ai_benchmark_job`	Stops a running AI benchmark job
`stop_ai_recommendation_job`	Stops a running AI recommendation job
`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_job`	Stops a running job
`stop_labeling_job`	Stops a running labeling job
`stop_mlflow_tracking_server`	Programmatically stop an MLflow Tracking Server
`stop_monitoring_schedule`	Stops a previously started monitoring schedule
`stop_notebook_instance`	Terminates the ML compute instance
`stop_optimization_job`	Ends a running inference optimization job
`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_cluster`	Updates a SageMaker HyperPod cluster
`update_cluster_scheduler_config`	Update the cluster policy configuration
`update_cluster_software`	Updates the platform software of a SageMaker HyperPod cluster for security patching
`update_code_repository`	Updates the specified Git repository with the specified values
`update_compute_quota`	Update the compute allocation definition
`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 EndpointConfig specified in the request to a new fleet of instances
`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_hub_content`	Updates SageMaker hub content (either a Model or Notebook resource)
`update_hub_content_reference`	Updates the contents of a SageMaker hub for a ModelReference resource
`update_image`	Updates the properties of a SageMaker AI image
`update_image_version`	Updates the properties of a SageMaker AI image version
`update_inference_component`	Updates an inference component
`update_inference_component_runtime_config`	Runtime settings for a model that is deployed with an inference component
`update_inference_experiment`	Updates an inference experiment that you created
`update_mlflow_app`	Updates an MLflow App
`update_mlflow_tracking_server`	Updates properties of an existing MLflow Tracking Server
`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_partner_app`	Updates all of the SageMaker Partner AI Apps in an account
`update_pipeline`	Updates a pipeline
`update_pipeline_execution`	Updates a pipeline execution
`update_pipeline_version`	Updates a pipeline version
`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¶

svc <- sagemaker()
svc$add_association(
  Foo = 123
)