@BetaApi(
      "The surface for long-running operations is not stable yet and may change in the future.")
  public final OperationFuture<Empty, OperationMetadata> deployModelAsync(String name) {

    DeployModelRequest request = DeployModelRequest.newBuilder().setName(name).build();
    return deployModelAsync(request);
  }
@ BetaApi ( "The surface for long-running operations is not stable yet and may change in the future." ) public final OperationFuture < Empty , OperationMetadata > deployModelAsync ( String name ) { DeployModelRequest request = DeployModelRequest . newBuilder ( ) . setName ( name ) . build ( ) ; return deployModelAsync ( request ) ; }
Deploys a model. If a model is already deployed, deploying it with the same parameters has no
effect. Deploying with different parametrs (as e.g. changing

<p>[node_number][google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata.node_number]
) will update the deployment without pausing the model's availability.

<p>Only applicable for Text Classification, Image Object Detection and Tables; all other
domains manage deployment automatically.

<p>Returns an empty response in the [response][google.longrunning.Operation.response] field
when it completes.

<p>Sample code:

<pre><code>
try (AutoMlClient autoMlClient = AutoMlClient.create()) {
ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
autoMlClient.deployModelAsync(name.toString()).get();
}
</code></pre>

@param name Resource name of the model to deploy.
@throws com.google.api.gax.rpc.ApiException if the remote call fails