Implementa un modelo

Demuestra cómo implementar un modelo.

Páginas de documentación que incluyen esta muestra de código

Para ver la muestra de código usada en contexto, consulta la siguiente documentación:

Muestra de código

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.DeployModelRequest;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.OperationMetadata;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeployModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    deployModel(projectId, modelId);
  }

  // Deploy a model for prediction
  static void deployModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      DeployModelRequest request =
          DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
      OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);

      future.get();
      System.out.println("Model deployment finished");
    }
  }
}

Node.js

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to deploy model.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Deploy a model with the deploy model request.
client
  .deployModel({name: modelFullId})
  .then(responses => {
    const response = responses[0];
    console.log('Deployment Details:');
    console.log(`\tName: ${response.name}`);
    console.log('\tMetadata:');
    console.log(`\t\tType Url: ${response.metadata.typeUrl}`);
    console.log(`\tDone: ${response.done}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Deploy model
response = client.deploy_model(model_display_name=model_display_name)

# synchronous check of operation status.
print("Model deployed. {}".format(response.result()))

¿Qué sigue?

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