CreateEndpoint

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Note
Use this API only for hosting models using Amazon SageMaker hosting services.
You must not delete an EndpointConfig 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 AWS Region in your AWS account.

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

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

{
   "[EndpointConfigName](#SageMaker-CreateEndpoint-request-EndpointConfigName)": "string",
   "[EndpointName](#SageMaker-CreateEndpoint-request-EndpointName)": "string",
   "[Tags](#SageMaker-CreateEndpoint-request-Tags)": [ 
      { 
         "[Key](API_Tag.md#SageMaker-Type-Tag-Key)": "string",
         "[Value](API_Tag.md#SageMaker-Type-Tag-Value)": "string"
      }
   ]
}

For information about the parameters that are common to all actions, see Common Parameters.

The request accepts the following data in JSON format.

** EndpointConfigName ** The name of an endpoint configuration. For more information, see CreateEndpointConfig.
Type: String
Length Constraints: Maximum length of 63.
Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9])*
Required: Yes

** EndpointName ** The name of the endpoint. The name must be unique within an AWS Region in your AWS account.
Type: String
Length Constraints: Maximum length of 63.
Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9])*
Required: Yes

** Tags ** An array of key-value pairs. For more information, see Using Cost Allocation Tagsin the AWS Billing and Cost Management User Guide.
Type: Array of Tag objects
Array Members: Minimum number of 0 items. Maximum number of 50 items.
Required: No

{
   "[EndpointArn](#SageMaker-CreateEndpoint-response-EndpointArn)": "string"
}

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.

** EndpointArn ** The Amazon Resource Name (ARN) of the endpoint.
Type: String
Length Constraints: Minimum length of 20. Maximum length of 2048.
Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:endpoint/.*

For information about the errors that are common to all actions, see Common Errors.

ResourceLimitExceeded
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
HTTP Status Code: 400

For more information about using this API in one of the language-specific AWS SDKs, see the following: + AWS Command Line Interface + AWS SDK for .NET + AWS SDK for C++ + AWS SDK for Go + AWS SDK for Go - Pilot + AWS SDK for Java + AWS SDK for JavaScript + AWS SDK for PHP V3 + AWS SDK for Python + AWS SDK for Ruby V2