diff --git a/automl/vision/automlVisionDataset.js b/automl/vision/automlVisionDataset.js
index 67cfa5d6f89..aec1b6dbc8f 100644
--- a/automl/vision/automlVisionDataset.js
+++ b/automl/vision/automlVisionDataset.js
@@ -1,17 +1,16 @@
-/**
- * Copyright 2018, Google, LLC
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
+// Copyright 2018 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 
 /**
  * This application demonstrates how to perform basic operations on dataset
@@ -23,238 +22,273 @@
 
 `use strict`;
 
-async function createDataset(
-  projectId,
-  computeRegion,
-  datasetName,
-  multiLabel
-) {
+function createDataset(projectId, computeRegion, datasetName, multiLabel) {
   // [START automl_vision_create_dataset]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetName = `name of the dataset to create, e.g. “myDataset”`;
-  // const multiLabel = `type of classification problem, true for multilabel and false for multiclass e.g. "false"`;
-
-  // A resource that represents Google Cloud Platform location.
-  const projectLocation = client.locationPath(projectId, computeRegion);
-
-  // Classification type is assigned based on multilabel value.
-  let classificationType = `MULTICLASS`;
-  if (multiLabel) {
-    classificationType = `MULTILABEL`;
+  async function automlVisionCreateDataset() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetName = `name of the dataset to create, e.g. “myDataset”`;
+    // const multiLabel = `type of classification problem, true for multilabel and false for multiclass e.g. "false"`;
+
+    // A resource that represents Google Cloud Platform location.
+    const projectLocation = client.locationPath(projectId, computeRegion);
+
+    // Classification type is assigned based on multilabel value.
+    let classificationType = `MULTICLASS`;
+    if (multiLabel) {
+      classificationType = `MULTILABEL`;
+    }
+
+    // Specify the text classification type for the dataset.
+    const datasetMetadata = {
+      classificationType: classificationType,
+    };
+
+    // Set dataset name and metadata.
+    const myDataset = {
+      displayName: datasetName,
+      imageClassificationDatasetMetadata: datasetMetadata,
+    };
+
+    // Create a dataset with the dataset metadata in the region.
+    const [dataset] = await client.createDataset({
+      parent: projectLocation,
+      dataset: myDataset,
+    });
+    // Display the dataset information.
+    console.log(`Dataset name: ${dataset.name}`);
+    console.log(`Dataset id: ${dataset.name.split(`/`).pop(-1)}`);
+    console.log(`Dataset display name: ${dataset.displayName}`);
+    console.log(`Dataset example count: ${dataset.exampleCount}`);
+    console.log(`Image Classification type:`);
+    console.log(
+      `\t ${dataset.imageClassificationDatasetMetadata.classificationType}`
+    );
+    console.log(`Dataset create time:`);
+    console.log(`\tseconds: ${dataset.createTime.seconds}`);
+    console.log(`\tnanos: ${dataset.createTime.nanos}`);
   }
 
-  // Specify the text classification type for the dataset.
-  const datasetMetadata = {
-    classificationType: classificationType,
-  };
-
-  // Set dataset name and metadata.
-  const myDataset = {
-    displayName: datasetName,
-    imageClassificationDatasetMetadata: datasetMetadata,
-  };
-
-  // Create a dataset with the dataset metadata in the region.
-  const [dataset] = await client.createDataset({
-    parent: projectLocation,
-    dataset: myDataset,
-  });
-  // Display the dataset information.
-  console.log(`Dataset name: ${dataset.name}`);
-  console.log(`Dataset id: ${dataset.name.split(`/`).pop(-1)}`);
-  console.log(`Dataset display name: ${dataset.displayName}`);
-  console.log(`Dataset example count: ${dataset.exampleCount}`);
-  console.log(`Image Classification type:`);
-  console.log(
-    `\t ${dataset.imageClassificationDatasetMetadata.classificationType}`
-  );
-  console.log(`Dataset create time:`);
-  console.log(`\tseconds: ${dataset.createTime.seconds}`);
-  console.log(`\tnanos: ${dataset.createTime.nanos}`);
+  automlVisionCreateDataset().catch(console.error);
   // [END automl_vision_create_dataset]
 }
 
-async function listDatasets(projectId, computeRegion, filter) {
+function listDatasets(projectId, computeRegion, filter) {
   // [START automl_vision_list_datasets]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const filter = `filter expressions, must specify field e.g. “imageClassificationModelMetadata:*”`;
-
-  // A resource that represents Google Cloud Platform location.
-  const projectLocation = client.locationPath(projectId, computeRegion);
-
-  // List all the datasets available in the region by applying filter.
-  const [datasets] = await client.listDatasets({
-    parent: projectLocation,
-    filter: filter,
-  });
-  console.log(`List of datasets:`);
-  datasets.forEach(dataset => {
+  async function automlVisionListDatasets() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const filter = `filter expressions, must specify field e.g. “imageClassificationModelMetadata:*”`;
+
+    // A resource that represents Google Cloud Platform location.
+    const projectLocation = client.locationPath(projectId, computeRegion);
+
+    // List all the datasets available in the region by applying filter.
+    const [datasets] = await client.listDatasets({
+      parent: projectLocation,
+      filter: filter,
+    });
+    console.log(`List of datasets:`);
+    datasets.forEach(dataset => {
+      console.log(`Dataset name: ${dataset.name}`);
+      console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
+      console.log(`Dataset display name: ${dataset.displayName}`);
+      console.log(`Dataset example count: ${dataset.exampleCount}`);
+      console.log(`Image Classification type:`);
+      console.log(
+        `\t`,
+        dataset.imageClassificationDatasetMetadata.classificationType
+      );
+      console.log(`Dataset create time: `);
+      console.log(`\tseconds: ${dataset.createTime.seconds}`);
+      console.log(`\tnanos: ${dataset.createTime.nanos}`);
+      console.log(`\n`);
+    });
+  }
+
+  automlVisionListDatasets().catch(console.error);
+  // [END automl_vision_list_datasets]
+}
+
+function getDataset(projectId, computeRegion, datasetId) {
+  // [START automl_vision_get_dataset]
+  async function automlVisionGetDataset() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetId = `Id of the dataset`;
+
+    // Get the full path of the dataset.
+    const datasetFullId = client.datasetPath(
+      projectId,
+      computeRegion,
+      datasetId
+    );
+
+    // Get complete detail of the dataset.
+    const [dataset] = await client.getDataset({name: datasetFullId});
+    // Display the dataset information.
     console.log(`Dataset name: ${dataset.name}`);
     console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
     console.log(`Dataset display name: ${dataset.displayName}`);
     console.log(`Dataset example count: ${dataset.exampleCount}`);
-    console.log(`Image Classification type:`);
     console.log(
-      `\t`,
-      dataset.imageClassificationDatasetMetadata.classificationType
+      `Classification type: ${dataset.imageClassificationDatasetMetadata.classificationType}`
     );
     console.log(`Dataset create time: `);
     console.log(`\tseconds: ${dataset.createTime.seconds}`);
     console.log(`\tnanos: ${dataset.createTime.nanos}`);
-    console.log(`\n`);
-  });
-  // [END automl_vision_list_datasets]
-}
+  }
 
-async function getDataset(projectId, computeRegion, datasetId) {
-  // [START automl_vision_get_dataset]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetId = `Id of the dataset`;
-
-  // Get the full path of the dataset.
-  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
-
-  // Get complete detail of the dataset.
-  const [dataset] = await client.getDataset({name: datasetFullId});
-  // Display the dataset information.
-  console.log(`Dataset name: ${dataset.name}`);
-  console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
-  console.log(`Dataset display name: ${dataset.displayName}`);
-  console.log(`Dataset example count: ${dataset.exampleCount}`);
-  console.log(
-    `Classification type: ${dataset.imageClassificationDatasetMetadata.classificationType}`
-  );
-  console.log(`Dataset create time: `);
-  console.log(`\tseconds: ${dataset.createTime.seconds}`);
-  console.log(`\tnanos: ${dataset.createTime.nanos}`);
+  automlVisionGetDataset().catch(console.error);
   // [END automl_vision_get_dataset]
 }
 
-async function importData(projectId, computeRegion, datasetId, path) {
+function importData(projectId, computeRegion, datasetId, path) {
   // [START automl_vision_import_data]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetId = `Id of the dataset`;
-  // const path = `string or array of .csv paths in AutoML Vision CSV format, e.g. “gs://myproject/traindata.csv”;`
-
-  // Get the full path of the dataset.
-  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
-
-  // Get one or more Google Cloud Storage URI(s).
-  const inputUris = path.split(`,`);
-  const inputConfig = {
-    gcsSource: {
-      inputUris: inputUris,
-    },
-  };
-
-  // Import the dataset from the input URI.
-  const [operation] = await client.importData({
-    name: datasetFullId,
-    inputConfig: inputConfig,
-  });
-  console.log(`Processing import...`);
-
-  const [, , response] = await operation.promise();
+  async function automlVisionImportData() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetId = `Id of the dataset`;
+    // const path = `string or array of .csv paths in AutoML Vision CSV format, e.g. “gs://myproject/traindata.csv”;`
+
+    // Get the full path of the dataset.
+    const datasetFullId = client.datasetPath(
+      projectId,
+      computeRegion,
+      datasetId
+    );
 
-  // The final result of the operation.
-  if (response.done) {
-    console.log(`Data imported.`);
+    // Get one or more Google Cloud Storage URI(s).
+    const inputUris = path.split(`,`);
+    const inputConfig = {
+      gcsSource: {
+        inputUris: inputUris,
+      },
+    };
+
+    // Import the dataset from the input URI.
+    const [operation] = await client.importData({
+      name: datasetFullId,
+      inputConfig: inputConfig,
+    });
+    console.log(`Processing import...`);
+
+    const [, , response] = await operation.promise();
+
+    // The final result of the operation.
+    if (response.done) {
+      console.log(`Data imported.`);
+    }
   }
+
+  automlVisionImportData().catch(console.error);
   // [END automl_vision_import_data]
 }
 
-async function exportData(projectId, computeRegion, datasetId, outputUri) {
+function exportData(projectId, computeRegion, datasetId, outputUri) {
   // [START automl_vision_export_data]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetId = `Id of the dataset`;
-  // const outputUri = `Google Cloud Storage URI for the export directory, e.g. “gs://myproject/output”;`
-
-  // Get the full path of the dataset.
-  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
+  async function automlVisionExportData() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetId = `Id of the dataset`;
+    // const outputUri = `Google Cloud Storage URI for the export directory, e.g. “gs://myproject/output”;`
+
+    // Get the full path of the dataset.
+    const datasetFullId = client.datasetPath(
+      projectId,
+      computeRegion,
+      datasetId
+    );
 
-  // Set the output URI
-  const outputConfig = {
-    gcsDestination: {
-      outputUriPrefix: outputUri,
-    },
-  };
-
-  // Export the data to the output URI.
-  const [operation] = await client.exportData({
-    name: datasetFullId,
-    outputConfig: outputConfig,
-  });
-  const [, , response] = await operation.promise();
-
-  // The final result of the operation.
-  if (response.done) {
-    console.log(`Data exported.`);
+    // Set the output URI
+    const outputConfig = {
+      gcsDestination: {
+        outputUriPrefix: outputUri,
+      },
+    };
+
+    // Export the data to the output URI.
+    const [operation] = await client.exportData({
+      name: datasetFullId,
+      outputConfig: outputConfig,
+    });
+    const [, , response] = await operation.promise();
+
+    // The final result of the operation.
+    if (response.done) {
+      console.log(`Data exported.`);
+    }
   }
+
+  automlVisionExportData().catch(console.error);
   // [END automl_vision_export_data]
 }
 
-async function deleteDataset(projectId, computeRegion, datasetId) {
+function deleteDataset(projectId, computeRegion, datasetId) {
   // [START automl_vision_delete_dataset]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetId = `Id of the dataset`;
-
-  // Get the full path of the dataset.
-  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
-
-  // Delete a dataset.
-  const [operation] = await client.deleteDataset({name: datasetFullId});
-  const [, , response] = await operation.promise();
-  // The final result of the operation.
-  if (response.done) {
-    console.log(`Dataset deleted.`);
+  async function automlVisionDeleteDataset() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetId = `Id of the dataset`;
+
+    // Get the full path of the dataset.
+    const datasetFullId = client.datasetPath(
+      projectId,
+      computeRegion,
+      datasetId
+    );
+
+    // Delete a dataset.
+    const [operation] = await client.deleteDataset({name: datasetFullId});
+    const [, , response] = await operation.promise();
+    // The final result of the operation.
+    if (response.done) {
+      console.log(`Dataset deleted.`);
+    }
   }
+
+  automlVisionDeleteDataset().catch(console.error);
   // [END automl_vision_delete_dataset]
 }
 
diff --git a/automl/vision/automlVisionModel.js b/automl/vision/automlVisionModel.js
index 96c52c84c02..ff04194f93e 100644
--- a/automl/vision/automlVisionModel.js
+++ b/automl/vision/automlVisionModel.js
@@ -1,17 +1,16 @@
-/**
- * Copyright 2018, Google, LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
+// Copyright 2018 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 
 /**
  * This application demonstrates how to perform basic operations on dataset
@@ -23,7 +22,7 @@
 
 `use strict`;
 
-async function createModel(
+function createModel(
   projectId,
   computeRegion,
   datasetId,
@@ -31,108 +30,188 @@ async function createModel(
   trainBudget
 ) {
   // [START automl_vision_create_model]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
+  async function automlVisionCreateModel() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const datasetId = `Id of the dataset`;
+    // const modelName = `Name of the model, e.g. "myModel"`;
+    // const trainBudget = `Budget for training model, e.g. 50`;
+
+    // A resource that represents Google Cloud Platform location.
+    const projectLocation = client.locationPath(projectId, computeRegion);
+
+    // Check train budget condition.
+    if (trainBudget === 0) {
+      trainBudget = {};
+    } else {
+      trainBudget = {trainBudget: trainBudget};
+    }
 
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const datasetId = `Id of the dataset`;
-  // const modelName = `Name of the model, e.g. "myModel"`;
-  // const trainBudget = `Budget for training model, e.g. 50`;
-
-  // A resource that represents Google Cloud Platform location.
-  const projectLocation = client.locationPath(projectId, computeRegion);
-
-  // Check train budget condition.
-  if (trainBudget === 0) {
-    trainBudget = {};
-  } else {
-    trainBudget = {trainBudget: trainBudget};
-  }
+    // Set model name and model metadata for the dataset.
+    const myModel = {
+      displayName: modelName,
+      datasetId: datasetId,
+      imageClassificationModelMetadata: trainBudget,
+    };
+
+    // Create a model with the model metadata in the region.
+    const [operation, initialApiResponse] = await client.createModel({
+      parent: projectLocation,
+      model: myModel,
+    });
+    console.log(`Training operation name: `, initialApiResponse.name);
+    console.log(`Training started...`);
+    const [model] = await operation.promise();
+
+    // Retrieve deployment state.
+    let deploymentState = ``;
+    if (model.deploymentState === 1) {
+      deploymentState = `deployed`;
+    } else if (model.deploymentState === 2) {
+      deploymentState = `undeployed`;
+    }
 
-  // Set model name and model metadata for the dataset.
-  const myModel = {
-    displayName: modelName,
-    datasetId: datasetId,
-    imageClassificationModelMetadata: trainBudget,
-  };
-
-  // Create a model with the model metadata in the region.
-  const [operation, initialApiResponse] = await client.createModel({
-    parent: projectLocation,
-    model: myModel,
-  });
-  console.log(`Training operation name: `, initialApiResponse.name);
-  console.log(`Training started...`);
-  const [model] = await operation.promise();
-
-  // Retrieve deployment state.
-  let deploymentState = ``;
-  if (model.deploymentState === 1) {
-    deploymentState = `deployed`;
-  } else if (model.deploymentState === 2) {
-    deploymentState = `undeployed`;
+    // Display the model information.
+    console.log(`Model name: ${model.name}`);
+    console.log(`Model id: ${model.name.split(`/`).pop(-1)}`);
+    console.log(`Model display name: ${model.displayName}`);
+    console.log(`Model create time:`);
+    console.log(`\tseconds: ${model.createTime.seconds}`);
+    console.log(`\tnanos: ${model.createTime.nanos}`);
+    console.log(`Model deployment state: ${deploymentState}`);
   }
 
-  // Display the model information.
-  console.log(`Model name: ${model.name}`);
-  console.log(`Model id: ${model.name.split(`/`).pop(-1)}`);
-  console.log(`Model display name: ${model.displayName}`);
-  console.log(`Model create time:`);
-  console.log(`\tseconds: ${model.createTime.seconds}`);
-  console.log(`\tnanos: ${model.createTime.nanos}`);
-  console.log(`Model deployment state: ${deploymentState}`);
+  automlVisionCreateModel().catch(console.error);
   // [END automl_vision_create_model]
 }
 
-async function getOperationStatus(operationFullId) {
+function getOperationStatus(operationFullId) {
   // [START automl_vision_get_operation_status]
-  const automl = require(`@google-cloud/automl`).v1beta1;
+  async function automlVisionGetOperationStatus() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
 
-  const client = new automl.AutoMlClient();
+    const client = new automl.AutoMlClient();
 
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const operationFullId = `Full name of an operation, eg. “Projects/<projectId>/locations/us-central1/operations/<operationId>
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const operationFullId = `Full name of an operation, eg. “Projects/<projectId>/locations/us-central1/operations/<operationId>
 
-  // Get the latest state of a long-running operation.
-  const [response] = await client.operationsClient.getOperation(
-    operationFullId
-  );
-  console.log(`Operation status: `, response);
+    // Get the latest state of a long-running operation.
+    const [response] = await client.operationsClient.getOperation(
+      operationFullId
+    );
+    console.log(`Operation status: `, response);
+  }
+
+  automlVisionGetOperationStatus().catch(console.error);
   // [END automl_vision_get_operation_status]
 }
 
-async function listModels(projectId, computeRegion, filter) {
+function listModels(projectId, computeRegion, filter) {
   // [START automl_vision_list_models]
-  const automl = require(`@google-cloud/automl`);
+  async function automlVisinListModels() {
+    const automl = require(`@google-cloud/automl`);
+
+    const client = new automl.v1beta1.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
+
+    // A resource that represents Google Cloud Platform location.
+    const projectLocation = client.locationPath(projectId, computeRegion);
+
+    // List all the models available in the region by applying filter.
+    const [models] = await client.listModels({
+      parent: projectLocation,
+      filter: filter,
+    });
+
+    // Display the model information.
+    console.log(`List of models:`);
+    models.forEach(model => {
+      console.log(`Model name: ${model.name}`);
+      console.log(`Model id: ${model.name.split(`/`).pop(-1)}`);
+      console.log(`Model display name: ${model.displayName}`);
+      console.log(`Model dataset id: ${model.datasetId}`);
+      if (model.modelMetadata === `translationModelMetadata`) {
+        console.log(`Translation model metadata:`);
+        console.log(
+          `\tBase model: ${model.translationModelMetadata.baseModel}`
+        );
+        console.log(
+          `\tSource language code: ${model.translationModelMetadata.sourceLanguageCode}`
+        );
+        console.log(
+          `\tTarget language code: ${model.translationModelMetadata.targetLanguageCode}`
+        );
+      } else if (model.modelMetadata === `textClassificationModelMetadata`) {
+        console.log(
+          `Text classification model metadata: ${model.textClassificationModelMetadata}`
+        );
+      } else if (model.modelMetadata === `imageClassificationModelMetadata`) {
+        console.log(`Image classification model metadata:`);
+        console.log(
+          `\tBase model id: ${model.imageClassificationModelMetadata.baseModelId}`
+        );
+        console.log(
+          `\tTrain budget: ${model.imageClassificationModelMetadata.trainBudget}`
+        );
+        console.log(
+          `\tTrain cost: ${model.imageClassificationModelMetadata.trainCost}`
+        );
+        console.log(
+          `\tStop reason: ${model.imageClassificationModelMetadata.stopReason}`
+        );
+      }
+      console.log(`Model create time:`);
+      console.log(`\tseconds: ${model.createTime.seconds}`);
+      console.log(`\tnanos: ${model.createTime.nanos}`);
+      console.log(`Model update time:`);
+      console.log(`\tseconds: ${model.updateTime.seconds}`);
+      console.log(`\tnanos: ${model.updateTime.nanos}`);
+      console.log(`Model deployment state: ${model.deploymentState}`);
+      console.log(`\n`);
+    });
+  }
 
-  const client = new automl.v1beta1.AutoMlClient();
+  automlVisinListModels().catch(console.error);
+  // [END automl_vision_list_models]
+}
 
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
+function getModel(projectId, computeRegion, modelId) {
+  // [START automl_vision_get_model]
 
-  // A resource that represents Google Cloud Platform location.
-  const projectLocation = client.locationPath(projectId, computeRegion);
+  async function automlVisionGetModel() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
 
-  // List all the models available in the region by applying filter.
-  const [models] = await client.listModels({
-    parent: projectLocation,
-    filter: filter,
-  });
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const modelId = `id of the model, e.g. “ICN12345”`;
+
+    // Get the full path of the model.
+    const modelFullId = client.modelPath(projectId, computeRegion, modelId);
 
-  // Display the model information.
-  console.log(`List of models:`);
-  models.forEach(model => {
+    // Get complete detail of the model.
+    const [model] = await client.getModel({name: modelFullId});
+
+    // Display the model information.
     console.log(`Model name: ${model.name}`);
     console.log(`Model id: ${model.name.split(`/`).pop(-1)}`);
     console.log(`Model display name: ${model.displayName}`);
@@ -172,98 +251,41 @@ async function listModels(projectId, computeRegion, filter) {
     console.log(`\tseconds: ${model.updateTime.seconds}`);
     console.log(`\tnanos: ${model.updateTime.nanos}`);
     console.log(`Model deployment state: ${model.deploymentState}`);
-    console.log(`\n`);
-  });
-  // [END automl_vision_list_models]
-}
-
-async function getModel(projectId, computeRegion, modelId) {
-  // [START automl_vision_get_model]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const modelId = `id of the model, e.g. “ICN12345”`;
-
-  // Get the full path of the model.
-  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
-
-  // Get complete detail of the model.
-  const [model] = await client.getModel({name: modelFullId});
-
-  // Display the model information.
-  console.log(`Model name: ${model.name}`);
-  console.log(`Model id: ${model.name.split(`/`).pop(-1)}`);
-  console.log(`Model display name: ${model.displayName}`);
-  console.log(`Model dataset id: ${model.datasetId}`);
-  if (model.modelMetadata === `translationModelMetadata`) {
-    console.log(`Translation model metadata:`);
-    console.log(`\tBase model: ${model.translationModelMetadata.baseModel}`);
-    console.log(
-      `\tSource language code: ${model.translationModelMetadata.sourceLanguageCode}`
-    );
-    console.log(
-      `\tTarget language code: ${model.translationModelMetadata.targetLanguageCode}`
-    );
-  } else if (model.modelMetadata === `textClassificationModelMetadata`) {
-    console.log(
-      `Text classification model metadata: ${model.textClassificationModelMetadata}`
-    );
-  } else if (model.modelMetadata === `imageClassificationModelMetadata`) {
-    console.log(`Image classification model metadata:`);
-    console.log(
-      `\tBase model id: ${model.imageClassificationModelMetadata.baseModelId}`
-    );
-    console.log(
-      `\tTrain budget: ${model.imageClassificationModelMetadata.trainBudget}`
-    );
-    console.log(
-      `\tTrain cost: ${model.imageClassificationModelMetadata.trainCost}`
-    );
-    console.log(
-      `\tStop reason: ${model.imageClassificationModelMetadata.stopReason}`
-    );
   }
-  console.log(`Model create time:`);
-  console.log(`\tseconds: ${model.createTime.seconds}`);
-  console.log(`\tnanos: ${model.createTime.nanos}`);
-  console.log(`Model update time:`);
-  console.log(`\tseconds: ${model.updateTime.seconds}`);
-  console.log(`\tnanos: ${model.updateTime.nanos}`);
-  console.log(`Model deployment state: ${model.deploymentState}`);
+
+  automlVisionGetModel().catch(console.error);
   // [END automl_vision_get_model]
 }
 
-async function listModelEvaluations(projectId, computeRegion, modelId, filter) {
+function listModelEvaluations(projectId, computeRegion, modelId, filter) {
   // [START automl_vision_list_model_evaluations]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-  const util = require(`util`);
-
-  const client = new automl.AutoMlClient();
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const modelId = `id of the model, e.g. “ICN12345”`;
-  // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
-
-  // Get the full path of the model.
-  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+  async function automlVisionListModelEvalution() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+    const util = require(`util`);
+
+    const client = new automl.AutoMlClient();
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const modelId = `id of the model, e.g. “ICN12345”`;
+    // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
+
+    // Get the full path of the model.
+    const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+
+    // List all the model evaluations in the model by applying filter.
+    const [elements] = await client.listModelEvaluations({
+      parent: modelFullId,
+      filter: filter,
+    });
+    elements.forEach(element => {
+      console.log(util.inspect(element, false, null));
+    });
+  }
 
-  // List all the model evaluations in the model by applying filter.
-  const [elements] = await client.listModelEvaluations({
-    parent: modelFullId,
-    filter: filter,
-  });
-  elements.forEach(element => {
-    console.log(util.inspect(element, false, null));
-  });
+  automlVisionListModelEvalution().catch(console.error);
   // [END automl_vision_list_model_evaluations]
 }
 
@@ -303,110 +325,121 @@ async function getModelEvaluation(
   // [END automl_vision_get_model_evaluation]
 }
 
-async function displayEvaluation(projectId, computeRegion, modelId, filter) {
+function displayEvaluation(projectId, computeRegion, modelId, filter) {
   // [START automl_vision_display_evaluation]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-  const math = require(`mathjs`);
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const modelId = `id of the model, e.g. “ICN12345”`;
-  // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
-
-  // Get the full path of the model.
-  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
-
-  // List all the model evaluations in the model by applying filter.
-  const [response] = await client.listModelEvaluations({
-    parent: modelFullId,
-    filter: filter,
-  });
-
-  response.forEach(async element => {
-    // There is evaluation for each class in a model and for overall model.
-    // Get only the evaluation of overall model.
-    if (!element.annotationSpecId) {
-      const modelEvaluationId = element.name.split(`/`).pop(-1);
-
-      // Resource name for the model evaluation.
-      const modelEvaluationFullId = client.modelEvaluationPath(
-        projectId,
-        computeRegion,
-        modelId,
-        modelEvaluationId
-      );
+  async function automlVisionDisplayEvalution() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+    const math = require(`mathjs`);
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const modelId = `id of the model, e.g. “ICN12345”`;
+    // const filter = `filter expressions, must specify field, e.g. “imageClassificationModelMetadata:*”`;
+
+    // Get the full path of the model.
+    const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+
+    // List all the model evaluations in the model by applying filter.
+    const [response] = await client.listModelEvaluations({
+      parent: modelFullId,
+      filter: filter,
+    });
+    response.forEach(async element => {
+      // There is evaluation for each class in a model and for overall model.
+      // Get only the evaluation of overall model.
+      if (!element.annotationSpecId) {
+        const modelEvaluationId = element.name.split(`/`).pop(-1);
+
+        // Resource name for the model evaluation.
+        const modelEvaluationFullId = client.modelEvaluationPath(
+          projectId,
+          computeRegion,
+          modelId,
+          modelEvaluationId
+        );
+
+        let modelEvaluation = null;
+
+        (async () => {
+          [modelEvaluation] = await client.getModelEvaluation({
+            name: modelEvaluationFullId,
+          });
+        })();
+        const classMetrics = modelEvaluation.classificationEvaluationMetrics;
+        const confidenceMetricsEntries = classMetrics.confidenceMetricsEntry;
+
+        // Showing model score based on threshold of 0.5
+        confidenceMetricsEntries.forEach(confidenceMetricsEntry => {
+          if (confidenceMetricsEntry.confidenceThreshold === 0.5) {
+            console.log(
+              `Precision and recall are based on a score threshold of 0.5`
+            );
+            console.log(
+              `Model Precision: %`,
+              math.round(confidenceMetricsEntry.precision * 100, 2)
+            );
+            console.log(
+              `Model Recall: %`,
+              math.round(confidenceMetricsEntry.recall * 100, 2)
+            );
+            console.log(
+              `Model F1 score: %`,
+              math.round(confidenceMetricsEntry.f1Score * 100, 2)
+            );
+            console.log(
+              `Model Precision@1: %`,
+              math.round(confidenceMetricsEntry.precisionAt1 * 100, 2)
+            );
+            console.log(
+              `Model Recall@1: %`,
+              math.round(confidenceMetricsEntry.recallAt1 * 100, 2)
+            );
+            console.log(
+              `Model F1 score@1: %`,
+              math.round(confidenceMetricsEntry.f1ScoreAt1 * 100, 2)
+            );
+          }
+        });
+      }
+    });
+  }
 
-      const [modelEvaluation] = await client.getModelEvaluation({
-        name: modelEvaluationFullId,
-      });
-      const classMetrics = modelEvaluation.classificationEvaluationMetrics;
-      const confidenceMetricsEntries = classMetrics.confidenceMetricsEntry;
-
-      // Showing model score based on threshold of 0.5
-      confidenceMetricsEntries.forEach(confidenceMetricsEntry => {
-        if (confidenceMetricsEntry.confidenceThreshold === 0.5) {
-          console.log(
-            `Precision and recall are based on a score threshold of 0.5`
-          );
-          console.log(
-            `Model Precision: %`,
-            math.round(confidenceMetricsEntry.precision * 100, 2)
-          );
-          console.log(
-            `Model Recall: %`,
-            math.round(confidenceMetricsEntry.recall * 100, 2)
-          );
-          console.log(
-            `Model F1 score: %`,
-            math.round(confidenceMetricsEntry.f1Score * 100, 2)
-          );
-          console.log(
-            `Model Precision@1: %`,
-            math.round(confidenceMetricsEntry.precisionAt1 * 100, 2)
-          );
-          console.log(
-            `Model Recall@1: %`,
-            math.round(confidenceMetricsEntry.recallAt1 * 100, 2)
-          );
-          console.log(
-            `Model F1 score@1: %`,
-            math.round(confidenceMetricsEntry.f1ScoreAt1 * 100, 2)
-          );
-        }
-      });
-    }
-  });
+  automlVisionDisplayEvalution().catch(console.error);
   // [END automl_vision_display_evaluation]
 }
 
-async function deleteModel(projectId, computeRegion, modelId) {
+function deleteModel(projectId, computeRegion, modelId) {
   // [START automl_vision_delete_model]
-  const automl = require(`@google-cloud/automl`).v1beta1;
-
-  const client = new automl.AutoMlClient();
-
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const modelId = `id of the model, e.g. “ICN12345”`;
-
-  // Get the full path of the model.
-  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
-
-  // Delete a model.
-  const [operation] = await client.deleteModel({name: modelFullId});
-  const [, , response] = await operation.promise();
-  // The final result of the operation.
-  if (response.done) {
-    console.log(`Model deleted.`);
+  async function automlVisionDeleteModel() {
+    const automl = require(`@google-cloud/automl`).v1beta1;
+
+    const client = new automl.AutoMlClient();
+
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const modelId = `id of the model, e.g. “ICN12345”`;
+
+    // Get the full path of the model.
+    const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+
+    // Delete a model.
+    const [operation] = await client.deleteModel({name: modelFullId});
+    const [, , response] = await operation.promise();
+    // The final result of the operation.
+    if (response.done) {
+      console.log(`Model deleted.`);
+    }
   }
+
+  automlVisionDeleteModel().catch(console.error);
   // [END automl_vision_delete_model]
 }
 
diff --git a/automl/vision/automlVisionPredict.js b/automl/vision/automlVisionPredict.js
index 50f7db73590..d48cdfeded2 100644
--- a/automl/vision/automlVisionPredict.js
+++ b/automl/vision/automlVisionPredict.js
@@ -1,17 +1,16 @@
-/**
- * Copyright 2018, Google, LLC.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
+// Copyright 2018 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 
 /**
  * This application demonstrates how to perform basic operations on dataset
@@ -23,57 +22,55 @@
 
 'use strict';
 
-async function predict(
-  projectId,
-  computeRegion,
-  modelId,
-  filePath,
-  scoreThreshold
-) {
+function predict(projectId, computeRegion, modelId, filePath, scoreThreshold) {
   // [START automl_vision_predict]
-  const automl = require('@google-cloud/automl').v1beta1;
-  const fs = require('fs');
+  async function automlVisionPredict() {
+    const automl = require('@google-cloud/automl').v1beta1;
+    const fs = require('fs');
 
-  // Create client for prediction service.
-  const client = new automl.PredictionServiceClient();
+    // Create client for prediction service.
+    const client = new automl.PredictionServiceClient();
 
-  /**
-   * TODO(developer): Uncomment the following line before running the sample.
-   */
-  // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
-  // const computeRegion = `region-name, e.g. "us-central1"`;
-  // const modelId = `id of the model, e.g. “ICN12345”`;
-  // const filePath = `local text file path of content to be classified, e.g. "./resources/test.txt"`;
-  // const scoreThreshold = `value between 0.0 and 1.0, e.g. "0.5"`;
+    /**
+     * TODO(developer): Uncomment the following line before running the sample.
+     */
+    // const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
+    // const computeRegion = `region-name, e.g. "us-central1"`;
+    // const modelId = `id of the model, e.g. “ICN12345”`;
+    // const filePath = `local text file path of content to be classified, e.g. "./resources/test.txt"`;
+    // const scoreThreshold = `value between 0.0 and 1.0, e.g. "0.5"`;
 
-  // Get the full path of the model.
-  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+    // Get the full path of the model.
+    const modelFullId = client.modelPath(projectId, computeRegion, modelId);
 
-  // Read the file content for prediction.
-  const content = fs.readFileSync(filePath, 'base64');
+    // Read the file content for prediction.
+    const content = fs.readFileSync(filePath, 'base64');
 
-  const params = {};
+    const params = {};
 
-  if (scoreThreshold) {
-    params.score_threshold = scoreThreshold;
-  }
+    if (scoreThreshold) {
+      params.score_threshold = scoreThreshold;
+    }
+
+    // Set the payload by giving the content and type of the file.
+    const payload = {};
+    payload.image = {imageBytes: content};
 
-  // Set the payload by giving the content and type of the file.
-  const payload = {};
-  payload.image = {imageBytes: content};
+    // params is additional domain-specific parameters.
+    // currently there is no additional parameters supported.
+    const [response] = await client.predict({
+      name: modelFullId,
+      payload: payload,
+      params: params,
+    });
+    console.log(`Prediction results:`);
+    response.payload.forEach(result => {
+      console.log(`Predicted class name: ${result.displayName}`);
+      console.log(`Predicted class score: ${result.classification.score}`);
+    });
+  }
 
-  // params is additional domain-specific parameters.
-  // currently there is no additional parameters supported.
-  const [response] = await client.predict({
-    name: modelFullId,
-    payload: payload,
-    params: params,
-  });
-  console.log(`Prediction results:`);
-  response.payload.forEach(result => {
-    console.log(`Predicted class name: ${result.displayName}`);
-    console.log(`Predicted class score: ${result.classification.score}`);
-  });
+  automlVisionPredict().catch(console.error);
   // [END automl_vision_predict]
 }