Creates an association between the source and the destination.

Adds or overwrites one or more tags for the specified SageMaker resource.

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies: https://aws.amazon.com/answers/account-management/aws-tagging-strategies/.

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html

Associates a trial component with a trial.

Creates an action.

Creates a running app for the specified UserProfile.

Creates a configuration for running a SageMaker image as a KernelGateway app.

Creates an artifact.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

We recommend using the new versions CreateAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html and DescribeAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html, which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

CreateAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html and DescribeAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html are new versions of CreateAutoMLJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html and DescribeAutoMLJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2: https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2.

For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html.

Creates a SageMaker HyperPod cluster.

Creates a Git repository as a resource in your SageMaker account.

You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

Starts a model compilation job.

After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

• A name for the compilation job

• Information about the input model artifacts

• The output location for the compiled model and the device (target) that the model runs on

• The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

Creates a context.

Creates a definition for a job that monitors data quality and drift.

Creates a Domain.

A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html.

VPC configuration

All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:

• PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.

• VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.

  When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.

Creates an edge deployment plan, consisting of multiple stages.

Starts a SageMaker Edge Manager model packaging job.

Creates an endpoint using the endpoint configuration specified in the request.

SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html API.

Use this API to deploy models using SageMaker hosting services.

You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.

When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When you call CreateEndpoint: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads : https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpointConfig.html before calling CreateEndpoint: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html to minimize the potential impact of a DynamoDB eventually consistent read.

When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region: https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html in the Amazon Web Services Identity and Access Management User Guide.

To add the IAM role policies for using this API operation, go to the IAM console: https://console.aws.amazon.com/iam/, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html and CreateEndpointConfig: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html API operations, add the following policies to the role.

Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

"Resource": [

"arn:aws:sagemaker:region:account-id:endpoint/endpointName"

"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

]

Creates an endpoint configuration that SageMaker hosting services uses to deploy models.

In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html API.

Use this API if you want to use SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Creates a SageMaker experiment.

An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.

In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.

The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to experiments, trials, trial components and then use the Search: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html API to search for the tags.

To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateExperiment.html API.

Create a new FeatureGroup.

A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.

The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas: https://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html to see the FeatureGroups quota for your Amazon Web Services account.

Note that it can take approximately 10-15 minutes to provision an OnlineStoreFeatureGroup with the InMemoryStorageType.

Create a hub.

Defines the settings you will use for the human review workflow user interface.

Starts a hyperparameter tuning job.

A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components: https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view.

Creates a custom SageMaker image.

Creates a version of the SageMaker image specified by ImageName.

Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint.

Creates an inference experiment using the configurations specified in the request.

Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests: https://docs.aws.amazon.com/sagemaker/latest/dg/shadow-tests.html.

Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.

Starts a recommendation job.

Creates a job that uses workers to label the data objects in your input dataset.

You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

• A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.

• One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.

• The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html.

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html.

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

Creates a model in SageMaker.

In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

For an example that calls this method when deploying a model to SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)).: https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints-deployment.html#realtime-endpoints-deployment-create-model

To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

Creates an Amazon SageMaker Model Card.

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.

Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

There are two types of model packages:

Versioned - a model that is part of a model group in the model registry.

Creates a model group.

Creates a definition for a job that monitors model quality and drift.

Creates an SageMaker notebook instance.

A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, SageMaker does the following:

1. Creates a network interface in the SageMaker VPC.

2. (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.

Creates a lifecycle configuration that you can associate with a notebook instance.

A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

Creates a URL for a specified UserProfile in a Domain.

When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.

The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.

You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint: https://docs.aws.amazon.com/sagemaker/latest/dg/studio-interface-endpoint.html .

Returns a URL that you can use to connect to the Jupyter server from a notebook instance.

In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.

You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address: https://docs.aws.amazon.com/sagemaker/latest/dg/security_iam_id-based-policy-examples.html#nbi-ip-filter.

Starts a model training job.

After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.

In the request body, you provide the following:

• AlgorithmSpecification - Identifies the training algorithm to use.

• HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html.

  Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

• InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.

• OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.

• ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

• EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html.

• RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.

• StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.

• Environment - The environment variables to set in the Docker container.

• RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

Starts a transform job.

A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

• TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

• ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html.

• TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

• TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

• TransformResources - Identifies the ML compute instances for the transform job.

Creates an SageMaker trial.

A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to a trial and then use the Search: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html API to search for the tags.

Creates a trial component, which is a stage of a machine learning trial.

A trial is composed of one or more trial components. A trial component can be used in multiple trials.

Trial components include pre-processing jobs, training jobs, and batch transform jobs.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

Creates a user profile.

Use this operation to create a workforce.

This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteWorkforce.html API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.

To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito): https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private.html.

Creates a new work team for labeling your data.

A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

Deletes an artifact.

Deletes the specified compilation job.

This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.

Used to delete a domain.

Deletes an endpoint.

SageMaker frees up all of the resources that were deployed when the endpoint was created.

SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant: http://docs.aws.amazon.com/kms/latest/APIReference/API_RevokeGrant.html API call.

Deletes an endpoint configuration.

The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

Deletes an SageMaker experiment.

Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup.

Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.

Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.

Delete a hub.

Delete the contents of a hub.

Use this operation to delete a human task user interface (worker task template).

Deletes a hyperparameter tuning job.

Deletes a SageMaker image and all versions of the image.

Deletes a version of a SageMaker image.

Deletes an inference experiment.

Deletes a model.

Deletes a model package.

Deletes a monitoring schedule.

Deletes an SageMaker notebook instance.

Before you can delete a notebook instance, you must call the StopNotebookInstance API.

Deletes a pipeline if there are no running instances of the pipeline.

Deletes the Amazon SageMaker Studio Lifecycle Configuration.

Deletes the specified tags from an SageMaker resource.

To list a resource's tags, use the ListTags API.

When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

Deletes the specified trial.

Deletes the specified trial component.

Deletes a user profile.

Use this operation to delete a workforce.

If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateWorkforce.html to create a new workforce.

Deletes an existing work team.

Deregisters the specified devices.

Returns information about an AutoML job created by calling CreateAutoMLJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html.

Returns information about a model compilation job.

Use this operation to describe a FeatureGroup.

Describe a hub.

Describe the content of a hub.

Returns a description of a hyperparameter tuning job, depending on the fields selected.

Provides the results of the Inference Recommender job.

Provides a list of properties for the requested lineage group.

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

Returns a description of a notebook instance lifecycle configuration.

Gets information about a work team provided by a vendor.

Returns information about a training job.

Describes a user profile.

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs: https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html).

Allowable IP address ranges are the IP addresses that workers can use to access tasks.

Gets information about a specific work team.

Disables using Service Catalog in SageMaker.

Disassociates a trial component from a trial.

This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AssociateTrialComponent.html API.

Enables using Service Catalog in SageMaker.

Gets a resource policy that manages access for a model group.

Gets the status of Service Catalog in SageMaker.

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.

An auto-complete API for the search functionality in the SageMaker console.

Import hub content.

Lists the AppImageConfigs in your account and their properties.

Lists model compilation jobs that satisfy various filters.

Lists all the experiments in your account.

List hub content versions.

List the contents of a hub.

List all existing hubs.

Lists the versions of a specified image and their properties.

Lists the images in your account and their properties.

Returns a list of the subtasks for an Inference Recommender job.

A list of lineage groups shared with your Amazon Web Services account.

Lists Amazon SageMaker Catalogs based on given filters and orders.

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.

Lists training jobs.

When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.

For example, if ListTrainingJobs is invoked with the following parameters:

{ ... MaxResults: 100, StatusEquals: InProgress ... }

First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.

You can quickly test the API using the following Amazon Web Services CLI code.

Lists the trial components in your account.

You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:

• ExperimentName

• SourceArn

• TrialName

Lists the trials in your account.