diff --git a/cloud-language/snippets/automl/.eslintrc.yml b/cloud-language/snippets/automl/.eslintrc.yml
new file mode 100644
index 0000000000..282535f55f
--- /dev/null
+++ b/cloud-language/snippets/automl/.eslintrc.yml
@@ -0,0 +1,3 @@
+---
+rules:
+  no-console: off
diff --git a/cloud-language/snippets/automl/automlNaturalLanguageDataset.js b/cloud-language/snippets/automl/automlNaturalLanguageDataset.js
new file mode 100755
index 0000000000..65c4db8b63
--- /dev/null
+++ b/cloud-language/snippets/automl/automlNaturalLanguageDataset.js
@@ -0,0 +1,393 @@
+/**
+ * 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.
+ */
+
+/**
+ * This application demonstrates how to perform basic operations on dataset
+ * with the Google AutoML Natural Language API.
+ *
+ * For more information, see the documentation at
+ * https://cloud.google.com/natural-language/automl/docs/
+ */
+
+`use strict`;
+
+function createDataset(projectId, computeRegion, datasetName, multilabel) {
+  // [START automl_natural_language_createDataset]
+  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 datasetName = `name of the dataset to create, e.g. “myDataset”`;
+  // const multiLabel = `type of the classification problem, e.g “False”, “True” (multilabel)`;
+
+  // 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`;
+  }
+
+  // Set dataset name and metadata.
+  const myDataset = {
+    displayName: datasetName,
+    textClassificationDatasetMetadata: {
+      classificationType: classificationType,
+    },
+  };
+
+  // Create a dataset with the dataset metadata in the region.
+  client
+    .createDataset({parent: projectLocation, dataset: myDataset})
+    .then(responses => {
+      const dataset = responses[0];
+
+      // 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(`Text classification type:`);
+      console.log(
+        `\t ${dataset.textClassificationDatasetMetadata.classificationType}`
+      );
+      console.log(`Dataset create time:`);
+      console.log(`\tseconds: ${dataset.createTime.seconds}`);
+      console.log(`\tnanos: ${dataset.createTime.nanos}`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_createDataset]
+}
+
+function listDatasets(projectId, computeRegion, filter) {
+  // [START automl_natural_language_listDatasets]
+  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 a Google Cloud Platform location.
+  const projectLocation = client.locationPath(projectId, computeRegion);
+
+  // List all the datasets available in the region by applying filter.
+  client
+    .listDatasets({parent: projectLocation, filter: filter})
+    .then(responses => {
+      const datasets = responses[0];
+
+      // Display the dataset information.
+      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(`Text classification type:`);
+        console.log(
+          `\t ${dataset.textClassificationDatasetMetadata.classificationType}`
+        );
+        console.log(`Dataset create time: `);
+        console.log(`\tseconds: ${dataset.createTime.seconds}`);
+        console.log(`\tnanos: ${dataset.createTime.nanos}`);
+        console.log(`\n`);
+      });
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_listDatasets]
+}
+
+function getDataset(projectId, computeRegion, datasetId) {
+  // [START automl_natural_language_getDataset]
+  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 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.
+  client
+    .getDataset({name: datasetFullId})
+    .then(responses => {
+      const dataset = responses[0];
+
+      // 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(
+        `Text classification type: ${
+          dataset.textClassificationDatasetMetadata.classificationType
+        }`
+      );
+      console.log(`Dataset create time: `);
+      console.log(`\tseconds: ${dataset.createTime.seconds}`);
+      console.log(`\tnanos: ${dataset.createTime.nanos}`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_getDataset]
+}
+
+function importData(projectId, computeRegion, datasetId, path) {
+  // [START automl_natural_language_importDataset]
+  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 datasetId = `Id of the dataset`;
+  // const path = `string or array of .csv paths in AutoML Vision CSV format, e.g. “gs://myproject/mytraindata.csv”;`
+
+  // Get the full path of the dataset.
+  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
+
+  // Get the multiple Google Cloud Storage URIs.
+  const inputUris = path.split(`,`);
+  const inputConfig = {
+    gcsSource: {
+      inputUris: inputUris,
+    },
+  };
+
+  // Import the dataset from the input URI.
+  client
+    .importData({name: datasetFullId, inputConfig: inputConfig})
+    .then(responses => {
+      const operation = responses[0];
+      console.log(`Processing import...`);
+      return operation.promise();
+    })
+    .then(responses => {
+      // The final result of the operation.
+      if (responses[2].done === true) console.log(`Data imported.`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_importDataset]
+}
+
+function exportData(projectId, computeRegion, datasetId, outputUri) {
+  // [START automl_natural_language_exportDataset]
+  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 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.
+  client
+    .exportData({name: datasetFullId, outputConfig: outputConfig})
+    .then(responses => {
+      const operation = responses[0];
+      console.log(`Processing export...`);
+      return operation.promise();
+    })
+    .then(responses => {
+      // The final result of the operation.
+      if (responses[2].done === true) console.log(`Data exported.`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_exportDataset]
+}
+
+function deleteDataset(projectId, computeRegion, datasetId) {
+  // [START automl_natural_language_deleteDataset]
+  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 datasetId = `Id of the dataset`;
+
+  // Get the full path of the dataset.
+  const datasetFullId = client.datasetPath(projectId, computeRegion, datasetId);
+
+  // Delete a dataset.
+  client
+    .deleteDataset({name: datasetFullId})
+    .then(responses => {
+      const operation = responses[0];
+      return operation.promise();
+    })
+    .then(responses => {
+      // The final result of the operation.
+      if (responses[2].done === true) console.log(`Dataset deleted.`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_deleteDataset]
+}
+
+require(`yargs`)
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: process.env.REGION_NAME,
+      requiresArg: true,
+      description: `region name e.g. "us-central1"`,
+    },
+    datasetName: {
+      alias: `n`,
+      type: `string`,
+      default: `testDataSet`,
+      requiresArg: true,
+      description: `Name of the Dataset`,
+    },
+    datasetId: {
+      alias: `i`,
+      type: `string`,
+      requiresArg: true,
+      description: `Id of the dataset`,
+    },
+    filter: {
+      alias: `f`,
+      default: `text_classification_dataset_metadata:*`,
+      type: `string`,
+      requiresArg: false,
+      description: `filter expression`,
+    },
+    multilabel: {
+      alias: `m`,
+      type: `string`,
+      default: false,
+      requiresArg: true,
+      description:
+        `Type of the classification problem, ` +
+        `False - MULTICLASS, True - MULTILABEL.`,
+    },
+    outputUri: {
+      alias: `o`,
+      type: `string`,
+      requiresArg: true,
+      description: `URI (or local path) to export dataset`,
+    },
+    path: {
+      alias: `p`,
+      type: `string`,
+      global: true,
+      default: `gs://nodejs-docs-samples-vcm/flowerTraindataMini.csv`,
+      requiresArg: true,
+      description: `URI or local path to input .csv, or array of .csv paths`,
+    },
+    projectId: {
+      alias: `z`,
+      type: `number`,
+      default: process.env.GCLOUD_PROJECT,
+      requiresArg: true,
+      description: `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`,
+    },
+  })
+  .command(`create-dataset`, `creates a new Dataset`, {}, opts =>
+    createDataset(
+      opts.projectId,
+      opts.computeRegion,
+      opts.datasetName,
+      opts.multilabel
+    )
+  )
+  .command(`list-datasets`, `list all Datasets`, {}, opts =>
+    listDatasets(opts.projectId, opts.computeRegion, opts.filter)
+  )
+  .command(`get-dataset`, `Get a Dataset`, {}, opts =>
+    getDataset(opts.projectId, opts.computeRegion, opts.datasetId)
+  )
+  .command(`delete-dataset`, `Delete a dataset`, {}, opts =>
+    deleteDataset(opts.projectId, opts.computeRegion, opts.datasetId)
+  )
+  .command(`import-data`, `Import labeled items into dataset`, {}, opts =>
+    importData(opts.projectId, opts.computeRegion, opts.datasetId, opts.path)
+  )
+  .command(
+    `export-data`,
+    `Export a dataset to a Google Cloud Storage Bucket`,
+    {},
+    opts =>
+      exportData(
+        opts.projectId,
+        opts.computeRegion,
+        opts.datasetId,
+        opts.outputUri
+      )
+  )
+  .example(`node $0 create-dataset -n "newDataSet"`)
+  .example(`node $0 list-datasets -f "imageClassificationDatasetMetadata:*"`)
+  .example(`node $0 get-dataset -i "DATASETID"`)
+  .example(`node $0 delete-dataset -i "DATASETID"`)
+  .example(
+    `node $0 import-data -i "dataSetId" -p "gs://myproject/mytraindata.csv"`
+  )
+  .example(
+    `node $0 export-data -i "dataSetId" -o "gs://myproject/outputdestination.csv"`
+  )
+  .wrap(120)
+  .recommendCommands()
+  .help()
+  .strict().argv;
diff --git a/cloud-language/snippets/automl/automlNaturalLanguageModel.js b/cloud-language/snippets/automl/automlNaturalLanguageModel.js
new file mode 100755
index 0000000000..a20472b8c9
--- /dev/null
+++ b/cloud-language/snippets/automl/automlNaturalLanguageModel.js
@@ -0,0 +1,608 @@
+/**
+ * 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.
+ */
+
+/**
+ * This application demonstrates how to perform basic operations on dataset
+ * with the Google AutoML Natural Language API.
+ *
+ * For more information, see the documentation at
+ * https://cloud.google.com/natural-language/automl/docs/
+ */
+
+`use strict`;
+
+function createModel(projectId, computeRegion, datasetId, modelName) {
+  // [START automl_natural_language_createModel]
+  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 datasetId = `Id of the dataset`;
+  // const modelName = `Name of the model, e.g. "myModel"`;
+
+  // A resource that represents Google Cloud Platform location.
+  const projectLocation = client.locationPath(projectId, computeRegion);
+
+  // Set model name and model metadata for the dataset.
+  const myModel = {
+    displayName: modelName,
+    datasetId: datasetId,
+    textClassificationModelMetadata: {},
+  };
+
+  // Create a model with the model metadata in the region.
+  client
+    .createModel({parent: projectLocation, model: myModel})
+    .then(responses => {
+      const operation = responses[0];
+      const initialApiResponse = responses[1];
+
+      console.log(`Training operation name: ${initialApiResponse.name}`);
+      console.log(`Training started...`);
+      return operation.promise();
+    })
+    .then(responses => {
+      // The final result of the operation.
+      const model = responses[0];
+
+      // 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}`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_createModel]
+}
+
+function getOperationStatus(operationFullId) {
+  // [START automl_natural_language_getOperationStatus]
+  const automl = require(`@google-cloud/automl`);
+
+  const client = new automl.v1beta1.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>
+  // Get the latest state of a long-running operation.
+
+  // Get the latest state of a long-running operation.
+  client.operationsClient.getOperation(operationFullId).then(responses => {
+    const response = responses[0];
+    console.log(`Operation status: ${response}`);
+  });
+  // [END automl_natural_language_getOperationStatus]
+}
+
+function listModels(projectId, computeRegion, filter) {
+  // [START automl_natural_language_listModels]
+  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.
+  if (filter === ``) filter = `textClassificationModelMetadata:*`;
+  client
+    .listModels({
+      parent: projectLocation,
+      filter: filter,
+    })
+    .then(responses => {
+      const models = responses[0];
+
+      // 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`);
+      });
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_listModels]
+}
+
+function getModel(projectId, computeRegion, modelId) {
+  // [START automl_natural_language_getModel]
+  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 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.
+  client
+    .getModel({name: modelFullId})
+    .then(responses => {
+      const model = responses[0];
+
+      // 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}`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_getModel]
+}
+
+function listModelEvaluations(projectId, computeRegion, modelId, filter_) {
+  // [START automl_natural_language_listModelEvaluations]
+  const automl = require(`@google-cloud/automl`);
+  const util = require(`util`);
+
+  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 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.
+  client
+    .listModelEvaluations({parent: modelFullId, filter: filter_})
+    .then(responses => {
+      const elements = responses[0];
+      console.log(`List of model evaluations:`);
+      elements.forEach(element => {
+        console.log(util.inspect(element, false, null));
+      });
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_listModelEvaluations]
+}
+
+function getModelEvaluation(
+  projectId,
+  computeRegion,
+  modelId,
+  modelEvaluationId
+) {
+  // [START automl_natural_language_getModelEvaluation]
+  const automl = require(`@google-cloud/automl`);
+  const util = require(`util`);
+
+  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 modelId = `id of the model, e.g. “ICN12345”`;
+  // const modelEvaluationId = `Id of your model evaluation, e.g “ICN12345”
+
+  // Get the full path of the model evaluation.
+  const modelEvaluationFullId = client.modelEvaluationPath(
+    projectId,
+    computeRegion,
+    modelId,
+    modelEvaluationId
+  );
+
+  // Get complete detail of the model evaluation.
+  client
+    .getModelEvaluation({name: modelEvaluationFullId})
+    .then(responses => {
+      const response = responses[0];
+      console.log(util.inspect(response, false, null));
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_getModelEvaluation]
+}
+
+function displayEvaluation(projectId, computeRegion, modelId, filter) {
+  // [START automl_natural_language_displayEvaluation]
+  const automl = require(`@google-cloud/automl`);
+  const math = require(`mathjs`);
+
+  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 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.
+  client
+    .listModelEvaluations({parent: modelFullId, filter: filter})
+    .then(respond => {
+      const response = respond[0];
+      response.forEach(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
+          );
+
+          // Get a model evaluation.
+          client
+            .getModelEvaluation({name: modelEvaluationFullId})
+            .then(responses => {
+              const modelEvaluation = responses[0];
+
+              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) + `%`
+                  );
+                }
+              });
+            })
+            .catch(err => {
+              console.error(err);
+            });
+        }
+      });
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_displayEvaluation]
+}
+
+function deleteModel(projectId, computeRegion, modelId) {
+  // [START automl_natural_language_deleteModel]
+  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 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.
+  client
+    .deleteModel({name: modelFullId})
+    .then(responses => {
+      const operation = responses[0];
+      return operation.promise();
+    })
+    .then(responses => {
+      // The final result of the operation.
+      if (responses[2].done === true) console.log(`Model deleted.`);
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_deleteModel]
+}
+
+require(`yargs`)
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: process.env.REGION_NAME,
+      requiresArg: true,
+      description: `region name e.g. "us-central1"`,
+    },
+    datasetId: {
+      alias: `i`,
+      type: `string`,
+      requiresArg: true,
+      description: `Id of the dataset`,
+    },
+    filter: {
+      alias: `f`,
+      default: ``,
+      type: `string`,
+      requiresArg: true,
+      description: `Name of the Dataset to search for`,
+    },
+    modelName: {
+      alias: `m`,
+      type: `string`,
+      default: false,
+      requiresArg: true,
+      description: `Name of the model`,
+    },
+    modelId: {
+      alias: `a`,
+      type: `string`,
+      default: ``,
+      requiresArg: true,
+      description: `Id of the model`,
+    },
+    modelEvaluationId: {
+      alias: `e`,
+      type: `string`,
+      default: ``,
+      requiresArg: true,
+      description: `Id of the model evaluation`,
+    },
+    operationFullId: {
+      alias: `o`,
+      type: `string`,
+      default: ``,
+      requiresArg: true,
+      description: `Full name of an operation`,
+    },
+    projectId: {
+      alias: `z`,
+      type: `number`,
+      default: process.env.GCLOUD_PROJECT,
+      requiresArg: true,
+      description: `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`,
+    },
+    trainBudget: {
+      alias: `t`,
+      type: `string`,
+      default: ``,
+      requiresArg: true,
+      description: `Budget for training the model`,
+    },
+  })
+  .command(`create-model`, `creates a new Model`, {}, opts =>
+    createModel(
+      opts.projectId,
+      opts.computeRegion,
+      opts.datasetId,
+      opts.modelName,
+      opts.trainBudget
+    )
+  )
+  .command(
+    `get-operation-status`,
+    `Gets status of current operation`,
+    {},
+    opts => getOperationStatus(opts.operationFullId)
+  )
+  .command(`list-models`, `list all Models`, {}, opts =>
+    listModels(opts.projectId, opts.computeRegion, opts.filter)
+  )
+  .command(`get-model`, `Get a Model`, {}, opts =>
+    getModel(opts.projectId, opts.computeRegion, opts.modelId)
+  )
+  .command(`list-model-evaluations`, `List model evaluations`, {}, opts =>
+    listModelEvaluations(
+      opts.projectId,
+      opts.computeRegion,
+      opts.modelId,
+      opts.filter
+    )
+  )
+  .command(`get-model-evaluation`, `Get model evaluation`, {}, opts =>
+    getModelEvaluation(
+      opts.projectId,
+      opts.computeRegion,
+      opts.modelId,
+      opts.modelEvaluationId
+    )
+  )
+  .command(`display-evaluation`, `Display evaluation`, {}, opts =>
+    displayEvaluation(
+      opts.projectId,
+      opts.computeRegion,
+      opts.modelId,
+      opts.filter
+    )
+  )
+  .command(`delete-model`, `Delete a Model`, {}, opts =>
+    deleteModel(opts.projectId, opts.computeRegion, opts.modelId)
+  )
+  .example(`node $0 create-model -i "DatasetID" -m "myModelName" -t "2"`)
+  .example(`node $0 get-operation-status -i "datasetId" -o "OperationFullID"`)
+  .example(`node $0 list-models -f "textClassificationModelMetadata:*"`)
+  .example(`node $0 get-model -a "ModelID"`)
+  .example(`node $0 list-model-evaluations -a "ModelID"`)
+  .example(`node $0 get-model-evaluation -a "ModelId" -e "ModelEvaluationID"`)
+  .example(`node $0 display-evaluation -a "ModelId"`)
+  .example(`node $0 delete-model -a "ModelID"`)
+  .wrap(120)
+  .recommendCommands()
+  .help()
+  .strict().argv;
diff --git a/cloud-language/snippets/automl/automlNaturalLanguagePredict.js b/cloud-language/snippets/automl/automlNaturalLanguagePredict.js
new file mode 100755
index 0000000000..bf0395e3e7
--- /dev/null
+++ b/cloud-language/snippets/automl/automlNaturalLanguagePredict.js
@@ -0,0 +1,126 @@
+/**
+ * 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.
+ */
+
+/**
+ * This application demonstrates how to perform basic operations on dataset
+ * with the Google AutoML Natural Language API.
+ *
+ * For more information, see the documentation at
+ * https://cloud.google.com/natural-language/automl/docs/
+ */
+
+`use strict`;
+
+function predict(projectId, computeRegion, modelId, filePath) {
+  // [START automl_natural_language_predict]
+  const automl = require(`@google-cloud/automl`);
+  const fs = require(`fs`);
+
+  // Create client for prediction service.
+  const client = new automl.v1beta1.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"`;
+
+  // Get the full path of the model.
+  const modelFullId = client.modelPath(projectId, computeRegion, modelId);
+
+  // Read the file content for prediction.
+  const snippet = fs.readFileSync(filePath, `utf8`);
+
+  // Set the payload by giving the content and type of the file.
+  const payload = {
+    textSnippet: {
+      content: snippet,
+      mimeType: `text/plain`,
+    },
+  };
+
+  // Params is additional domain-specific parameters.
+  // Currently there is no additional parameters supported.
+  client
+    .predict({name: modelFullId, payload: payload, params: {}})
+    .then(responses => {
+      console.log(`Prediction results:`);
+      responses[0].payload.forEach(result => {
+        console.log(`Predicted class name: ${result.displayName}`);
+        console.log(`Predicted class score: ${result.classification.score}`);
+      });
+    })
+    .catch(err => {
+      console.error(err);
+    });
+  // [END automl_natural_language_predict]
+}
+
+require(`yargs`)
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: process.env.REGION_NAME,
+      requiresArg: true,
+      description: `region name e.g. "us-central1"`,
+    },
+    filePath: {
+      alias: `f`,
+      default: `./resources/test.txt`,
+      type: `string`,
+      requiresArg: true,
+      description: `local text file path of the content to be classified`,
+    },
+    modelId: {
+      alias: `i`,
+      type: `string`,
+      requiresArg: true,
+      description: `Id of the model which will be used for text classification`,
+    },
+    projectId: {
+      alias: `z`,
+      type: `number`,
+      default: process.env.GCLOUD_PROJECT,
+      requiresArg: true,
+      description: `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`,
+    },
+    scoreThreshold: {
+      alias: `s`,
+      type: `string`,
+      default: `0.5`,
+      requiresArg: true,
+      description:
+        `A value from 0.0 to 1.0.  When the model makes predictions for an image it will` +
+        `only produce results that have at least this confidence score threshold.  Default is .5`,
+    },
+  })
+  .command(`predict`, `classify the content`, {}, opts =>
+    predict(
+      opts.projectId,
+      opts.computeRegion,
+      opts.modelId,
+      opts.filePath,
+      opts.scoreThreshold
+    )
+  )
+  .example(`node $0 predict -i "modelId" -f "./resources/test.txt" -s "0.5"`)
+  .wrap(120)
+  .recommendCommands()
+  .help()
+  .strict().argv;
diff --git a/cloud-language/snippets/automl/package.json b/cloud-language/snippets/automl/package.json
new file mode 100644
index 0000000000..8b9cc7a15b
--- /dev/null
+++ b/cloud-language/snippets/automl/package.json
@@ -0,0 +1,27 @@
+{
+  "name": "automl",
+  "version": "1.0.0",
+  "description": "",
+  "main": "automl_natural_language_dataset.js",
+  "scripts": {
+    "test": "mocha system-test/*.test.js --opts system-test/mocha.opts"
+  },
+  "engines": {
+    "node": ">=8.0.0"
+  },
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "@google-cloud/automl": "^0.1.1",
+    "@google-cloud/language": "^1.2.0",
+    "mathjs": "^5.0.4",
+    "util": "^0.11.0",
+    "yargs": "^12.0.1"
+  },
+  "devDependencies": {
+    "@google-cloud/nodejs-repo-tools": "^2.3.0",
+    "mocha": "^5.2.0",
+    "proxyquire": "^2.0.1",
+    "sinon": "^6.0.1"
+  }
+}
diff --git a/cloud-language/snippets/automl/resources/test.txt b/cloud-language/snippets/automl/resources/test.txt
new file mode 100644
index 0000000000..d742b01355
--- /dev/null
+++ b/cloud-language/snippets/automl/resources/test.txt
@@ -0,0 +1 @@
+A strong taste of hazlenut and orange
\ No newline at end of file
diff --git a/cloud-language/snippets/automl/system-test/.eslintrc.yml b/cloud-language/snippets/automl/system-test/.eslintrc.yml
new file mode 100644
index 0000000000..73f7bbc946
--- /dev/null
+++ b/cloud-language/snippets/automl/system-test/.eslintrc.yml
@@ -0,0 +1,5 @@
+---
+env:
+  mocha: true
+rules:
+  node/no-unpublished-require: off
diff --git a/cloud-language/snippets/automl/system-test/automlNaturalLanguage.test.js b/cloud-language/snippets/automl/system-test/automlNaturalLanguage.test.js
new file mode 100644
index 0000000000..33cf32d3f9
--- /dev/null
+++ b/cloud-language/snippets/automl/system-test/automlNaturalLanguage.test.js
@@ -0,0 +1,140 @@
+/**
+ * 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.
+ */
+
+'use strict';
+
+const assert = require(`assert`);
+const tools = require(`@google-cloud/nodejs-repo-tools`);
+
+const cmdDataset = `node automlNaturalLanguageDataset.js`;
+const cmdModel = `node automlNaturalLanguageModel.js`;
+const cmdPredict = `node automlNaturalLanguagePredict.js`;
+
+const testDataSetName = `testDataset`;
+const dummyDataSet = `dummyDataset`;
+const testModelName = `dummyModel`;
+const sampleText = `./resources/test.txt`;
+
+// Skipped because it's been taking too long to delete datasets
+it.skip(`should create a create, list, and delete a dataset`, async () => {
+  // Check to see that this dataset does not yet exist
+  let output = await tools.runAsync(`${cmdDataset} list-datasets`);
+  //t.false(output.includes(testDataSetName));
+  assert.notStrictEqual(RegExp(`testDataset`).test(output));
+
+  // Create dataset
+  output = await tools.runAsync(
+    `${cmdDataset} create-dataset -n "${testDataSetName}"`
+  );
+  const parsedOut = output.split(`\n`);
+  const dataSetId = parsedOut[1].split(`:`)[1].trim();
+  assert(RegExp(`Dataset display name:  testDataset`).test(output));
+
+  // Delete dataset
+  output = await tools.runAsync(
+    `${cmdDataset} delete-dataset -i "${dataSetId}"`
+  );
+  assert(RegExp(`Dataset deleted.`).test(output));
+});
+
+// See : https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/NaturalLanguage/automl/model_test.py
+// We make two models running this test, see hard-coded workaround below
+it.skip(`should create a dataset, import data, and start making a model`, async () => {
+  // Check to see that this dataset does not yet exist
+  let output = await tools.runAsync(`${cmdDataset} list-datasets`);
+  assert.notStrictEqual(RegExp(`dummyDataset`).test(output));
+
+  // Create dataset
+  output = await tools.runAsync(
+    `${cmdDataset} create-dataset -n "${dummyDataSet}"`
+  );
+
+  const dataSetId = output
+    .split(`\n`)[1]
+    .split(`:`)[1]
+    .trim();
+  assert(RegExp(`Dataset display name:  dummyDataSet`).test(output));
+
+  // Import Data
+  output = await tools.runAsync(
+    `${cmdDataset} import-data -i "${dataSetId}" -p "gs://nodejs-docs-samples-vcm/happiness.csv"`
+  );
+  assert(RegExp(`Data imported.`).test(output));
+
+  // Check to make sure model doesn't already exist
+  output = await tools.runAsync(`${cmdModel} list-models`);
+  assert.notStrictEqual(RegExp(`dummyModel`).test(output));
+
+  // Begin training dataset, getting operation ID for next operation
+  output = await tools.runAsync(
+    `${cmdModel} create-model -i "${dataSetId}" -m "${testModelName}" -t "2"`
+  );
+  const operationName = output
+    .split(`\n`)[0]
+    .split(`:`)[1]
+    .trim();
+  assert(RegExp(`Training started...`).test(output));
+
+  // Poll operation status, here confirming that operation is not complete yet
+  output = await tools.runAsync(
+    `${cmdModel} get-operation-status -i "${dataSetId}" -o "${operationName}"`
+  );
+  assert(RegExp(`done: false`).test(output));
+});
+
+it(`should display evaluation from prexisting model`, async () => {
+  const donotdeleteModelId = `TCN4740161257642267869`;
+
+  // Confirm dataset exists
+  let output = await tools.runAsync(`${cmdDataset} list-datasets`);
+  assert(RegExp(`dummyDb`).test(output));
+
+  // List model evaluations, confirm model exists
+  output = await tools.runAsync(
+    `${cmdModel} list-model-evaluations -a "${donotdeleteModelId}"`
+  );
+
+  // Display evaluation
+  output = await tools.runAsync(
+    `${cmdModel} display-evaluation -a "${donotdeleteModelId}"`
+  );
+  assert(RegExp(`Model Precision:`).test(output));
+});
+
+it(`should run Prediction from prexisting model`, async () => {
+  const donotdeleteModelId = `TCN4740161257642267869`;
+
+  // Confirm dataset exists
+  let output = await tools.runAsync(`${cmdDataset} list-datasets`);
+  assert(RegExp(`do_not_delete_me`).test(output));
+
+  // List model evaluations, confirm model exists
+  output = await tools.runAsync(
+    `${cmdModel} list-model-evaluations -a "${donotdeleteModelId}"`
+  );
+  assert(RegExp(`classificationEvaluationMetrics:`).test(output));
+
+  // Run prediction on 'test.txt' in resources folder
+  output = await tools.runAsync(
+    `${cmdPredict} predict -i "${donotdeleteModelId}" -f "${sampleText}" -s "0.5"`
+  );
+  assert(RegExp(`Firm_Cheese`).test(output));
+});
+
+// List datasets
+it(`should list datasets`, async () => {
+  const output = await tools.runAsync(`${cmdDataset} list-datasets`);
+  assert(RegExp(`List of datasets:`).test(output));
+});
diff --git a/cloud-language/snippets/automl/system-test/mocha.opts b/cloud-language/snippets/automl/system-test/mocha.opts
new file mode 100644
index 0000000000..f30d8e67d4
--- /dev/null
+++ b/cloud-language/snippets/automl/system-test/mocha.opts
@@ -0,0 +1,2 @@
+--timeout 20000
+--throw-deprecation
\ No newline at end of file
diff --git a/cloud-language/snippets/package.json b/cloud-language/snippets/package.json
index 20128ae3ef..68535b645b 100644
--- a/cloud-language/snippets/package.json
+++ b/cloud-language/snippets/package.json
@@ -11,6 +11,8 @@
     "test": "mocha"
   },
   "dependencies": {
+    "@google-cloud/automl": "^0.1.1",
+    "mathjs": "^5.1.0",
     "@google-cloud/language": "^2.0.0",
     "@google-cloud/storage": "^2.0.0",
     "yargs": "^12.0.0"