Menu
Amazon Machine Learning
API Reference (API Version 2014-12-12)

CreateRealtimeEndpoint

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

Request Syntax

Copy
{ "MLModelId": "string" }

Request Parameters

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

The request accepts the following data in JSON format.

MLModelId

The ID assigned to the MLModel during creation.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 64.

Pattern: [a-zA-Z0-9_.-]+

Required: Yes

Response Syntax

Copy
{ "MLModelId": "string", "RealtimeEndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number } }

Response Elements

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

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

MLModelId

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 64.

Pattern: [a-zA-Z0-9_.-]+

RealtimeEndpointInfo

The endpoint information of the MLModel

Type: RealtimeEndpointInfo object

Errors

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

InternalServerException

An error on the server occurred when trying to process a request.

HTTP Status Code: 500

InvalidInputException

An error on the client occurred. Typically, the cause is an invalid input value.

HTTP Status Code: 400

ResourceNotFoundException

A specified resource cannot be located.

HTTP Status Code: 400

Example

The following is a sample request and response of the CreateRealtimeEndpoint operation.

Sample Request

Copy
POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn-requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateRealtimeEndpoint { "MLModelId": "ml-ModelExampleId", }

Sample Response

Copy
HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "MLModelId": "ml-ModelExampleId", "EndpointInfo": { "CreatedAt": 1422488124.71, "EndpointUrl": "<realtime endpoint from Amazon Machine Learning for ml-ModelExampleId>", "EndpointStatus": "READY", "PeakRequestsPerSecond": 200 } }

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: