comprehend_create_endpoint: Creates a model-specific endpoint for synchronous inference...
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

Description Usage Arguments Value Request syntax

Creates a model-specific endpoint for synchronous inference for a previously trained custom model

1 2	comprehend_create_endpoint(EndpointName, ModelArn, DesiredInferenceUnits, ClientRequestToken, Tags)

`EndpointName`	[required] This is the descriptive suffix that becomes part of the `EndpointArn` used for all subsequent requests to this resource.
`ModelArn`	[required] The Amazon Resource Number (ARN) of the model to which the endpoint will be attached.
`DesiredInferenceUnits`	[required] The desired number of inference units to be used by the model using this endpoint. Each inference unit represents of a throughput of 100 characters per second.
`ClientRequestToken`	An idempotency token provided by the customer. If this token matches a previous endpoint creation request, Amazon Comprehend will not return a `ResourceInUseException`.
`Tags`	Tags associated with the endpoint being created. A tag is a key-value pair that adds metadata to the endpoint. For example, a tag with "Sales" as the key might be added to an endpoint to indicate its use by the sales department.

A list with the following syntax:

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list(
  EndpointArn = "string"
)

svc$create_endpoint(
  EndpointName = "string",
  ModelArn = "string",
  DesiredInferenceUnits = 123,
  ClientRequestToken = "string",
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)