create_realtime_endpoint

create_realtime_endpoint(**kwargs)

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 .

See also: AWS API Documentation

Request Syntax

response = client.create_realtime_endpoint(
    MLModelId='string'
)

Parameters

MLModelId (string) --

[REQUIRED]

The ID assigned to the MLModel during creation.

Return type

dict

Returns

Response Syntax

{
    'MLModelId': 'string',
    'RealtimeEndpointInfo': {
        'PeakRequestsPerSecond': 123,
        'CreatedAt': datetime(2015, 1, 1),
        'EndpointUrl': 'string',
        'EndpointStatus': 'NONE'|'READY'|'UPDATING'|'FAILED'
    }
}

Response Structure

• (dict) --

  Represents the output of an CreateRealtimeEndpoint operation.

  The result contains the MLModelId and the endpoint information for the MLModel .

  Note: The endpoint information includes the URI of the MLModel ; that is, the location to send online prediction requests for the specified MLModel .

  • MLModelId (string) --

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

  • RealtimeEndpointInfo (dict) --

    The endpoint information of the MLModel

    • PeakRequestsPerSecond (integer) --

      The maximum processing rate for the real-time endpoint for MLModel , measured in incoming requests per second.

    • CreatedAt (datetime) --

      The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

    • EndpointUrl (string) --

      The URI that specifies where to send real-time prediction requests for the MLModel .

      Note: The application must wait until the real-time endpoint is ready before using this URI.

    • EndpointStatus (string) --

      The current status of the real-time endpoint for the MLModel . This element can have one of the following values:

      • NONE - Endpoint does not exist or was previously deleted.
      • READY - Endpoint is ready to be used for real-time predictions.
      • UPDATING - Updating/creating the endpoint.

Exceptions

• MachineLearning.Client.exceptions.InvalidInputException
• MachineLearning.Client.exceptions.ResourceNotFoundException
• MachineLearning.Client.exceptions.InternalServerException