MachineLearning / Client / create_realtime_endpoint

create_realtime_endpoint#

MachineLearning.Client.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