[ aws . machinelearning ]

create-realtime-endpoint

Description

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

See ‘aws help’ for descriptions of global parameters.

Synopsis

  create-realtime-endpoint
--ml-model-id <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--ml-model-id (string)

The ID assigned to the MLModel during creation.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

See ‘aws help’ for descriptions of global parameters.

Output

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 -> (structure)

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 -> (timestamp)

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.