Amazon SageMaker Service
Provides APIs for creating and managing SageMaker resources.
Other Resources:
sagemaker( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config: Optional configuration of credentials, endpoint, and/or region.
credentials :
creds :
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 :
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.
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.
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"
)
| 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_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 |
| 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 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 a 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_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_labeling_job | Creates a job that uses workers to label the data objects in your input dataset |
| 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_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_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_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_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_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_cluster | Retrieves information of a SageMaker 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_labeling_job | Gets information about a labeling job |
| describe_lineage_group | Provides a list of properties for the requested lineage group |
| 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_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_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_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_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_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_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_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_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 |
| 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_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_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_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_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 |
## Not run: svc <- sagemaker() svc$add_association( Foo = 123 ) ## End(Not run)
Useful links