diff --git a/automl/package.json b/automl/package.json
index 5789b78810e..6fe011a2a6e 100644
--- a/automl/package.json
+++ b/automl/package.json
@@ -14,9 +14,17 @@
       "**/*.test.js"
     ]
   },
-  "scripts": {},
+  "scripts": {
+    "test": "mocha --timeout 600000"
+  },
   "dependencies": {
-    "@google-cloud/automl": "^0.1.3"
+    "@google-cloud/automl": "^0.1.3",
+    "chai": "^4.2.0",
+    "execa": "^1.0.0",
+    "mathjs": "^5.5.0",
+    "yargs": "^13.2.1"
   },
-  "devDependencies": {}
+  "devDependencies": {
+    "mocha": "^6.0.1"
+  }
 }
diff --git a/automl/system-test/.eslintrc.yml b/automl/system-test/.eslintrc.yml
deleted file mode 100644
index c0289282a68..00000000000
--- a/automl/system-test/.eslintrc.yml
+++ /dev/null
@@ -1,5 +0,0 @@
----
-rules:
-  node/no-unpublished-require: off
-  node/no-unsupported-features: off
-  no-empty: off
diff --git a/automl/test/.eslintrc.yml b/automl/test/.eslintrc.yml
new file mode 100644
index 00000000000..6db2a46c535
--- /dev/null
+++ b/automl/test/.eslintrc.yml
@@ -0,0 +1,3 @@
+---
+env:
+  mocha: true
diff --git a/automl/test/automlVision.test.js b/automl/test/automlVision.test.js
new file mode 100644
index 00000000000..16e2734afb1
--- /dev/null
+++ b/automl/test/automlVision.test.js
@@ -0,0 +1,139 @@
+/**
+ * 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 path = require('path');
+const {assert} = require('chai');
+const execa = require('execa');
+
+const exec = async cmd => (await execa.shell(cmd)).stdout;
+const cmdDataset = `node vision/automlVisionDataset.js`;
+const cmdModel = `node vision/automlVisionModel.js`;
+const cmdPredict = `node vision/automlVisionPredict.js`;
+
+const testDataSetName = `testDataSet`;
+const dummyDataSet = `dummyDataSet`;
+const testModelName = `dummyModel`;
+const testImgPath = `./resources/`;
+const sampleImage2 = path.join(testImgPath, `testImage2.jpg`);
+
+describe(`auto ml vision`, () => {
+  it.skip(`should create, list, and delete a dataset`, async () => {
+    // Check to see that this dataset does not yet exist
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.strictEqual(output.includes(testDataSetName), false);
+
+    // Create dataset
+    output = await exec(`${cmdDataset} create-dataset -n "${testDataSetName}"`);
+    const dataSetId = output
+      .split(`\n`)[1]
+      .split(`:`)[1]
+      .trim();
+    assert.match(output, new RegExp(testDataSetName));
+
+    // Delete dataset
+    output = await exec(`${cmdDataset} delete-dataset -i "${dataSetId}"`);
+    assert.match(output, /Dataset deleted./);
+  });
+
+  // See : https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/vision/automl/model_test.py
+  // We make two models running this test, see hard-coded workaround below
+  it.skip(`should create a dataset, import data, and start making a model`, async () => {
+    // Check to see that this dataset does not yet exist
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.strictEqual(output.includes(dummyDataSet), false);
+
+    // Create dataset
+    output = await exec(`${cmdDataset} create-dataset -n "${dummyDataSet}"`);
+    const dataSetId = output
+      .split(`\n`)[1]
+      .split(`:`)[1]
+      .trim();
+    assert.match(output, new RegExp(dummyDataSet));
+
+    // Import Data
+    output = await exec(
+      `${cmdDataset} import-data -i "${dataSetId}" -p "gs://nodejs-docs-samples-vcm/flowerTraindata20lines.csv"`
+    );
+    assert.match(output, /Data imported./);
+
+    // Check to make sure model doesn't already exist
+    output = await exec(`${cmdModel} list-models`);
+    assert.notMatch(output, new RegExp(testModelName));
+
+    // begin training dataset, getting operation ID for next operation
+    output = await exec(`
+      ${cmdModel} create-model -i "${dataSetId}" -m "${testModelName}" -t "2"`);
+    const operationName = output
+      .split(`\n`)[0]
+      .split(`:`)[1]
+      .split(`/`)
+      .pop()
+      .trim();
+    assert.match(output, /Training started.../);
+
+    // poll operation status, here confirming that operation is not complete yet
+    output = await exec(
+      `${cmdModel} get-operation-status -i "${dataSetId}" -o "${operationName}"`
+    );
+    assert.match(output, /done: false/);
+  });
+
+  it.skip(`should display evaluation from prexisting model`, async () => {
+    const flowersModelId = `ICN723541179344731436`;
+    const flowersDisplayName = `flowersTest`;
+
+    // Confirm dataset exists
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, new RegExp(flowersDisplayName));
+
+    // List model evaluations, confirm model exists
+    output = await exec(
+      `${cmdModel} list-model-evaluations -a "${flowersModelId}"`
+    );
+
+    // Display evaluation
+    output = await exec(
+      `${cmdModel} display-evaluation -a "${flowersModelId}"`
+    );
+    assert.match(output, /Model Precision/);
+  });
+
+  it.skip(`should run Prediction from prexisting model`, async () => {
+    const donotdeleteModelId = `ICN723541179344731436`;
+    const flowersDisplayName = `flowers`;
+
+    // Confirm dataset exists
+    let output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, new RegExp(flowersDisplayName));
+
+    // List model evaluations, confirm model exists
+    output = await exec(
+      `${cmdModel} list-model-evaluations -a "${donotdeleteModelId}"`
+    );
+    // Run prediction on 'testImage.jpg' in resources folder
+    output = await exec(
+      `${cmdPredict} predict -i "${donotdeleteModelId}" -f "${sampleImage2}" -s "0.5"`
+    );
+    assert.match(output, /dandelion/);
+  });
+
+  // List datasets
+  it(`should list datasets`, async () => {
+    const output = await exec(`${cmdDataset} list-datasets`);
+    assert.match(output, /List of datasets:/);
+  });
+});
diff --git a/automl/vision/automlVisionDataset.js b/automl/vision/automlVisionDataset.js
new file mode 100644
index 00000000000..5a4d8ead89c
--- /dev/null
+++ b/automl/vision/automlVisionDataset.js
@@ -0,0 +1,369 @@
+/**
+ * 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 Vision API.
+ *
+ * For more information, see the documentation at
+ * https://cloud.google.com/vision/automl/docs/
+ */
+
+`use strict`;
+
+async 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`;
+  }
+
+  // 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}`);
+  // [END automl_vision_create_dataset]
+}
+
+async 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 => {
+    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`);
+  });
+  // [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}`);
+  // [END automl_vision_get_dataset]
+}
+
+async 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();
+
+  // The final result of the operation.
+  if (response.done) {
+    console.log(`Data imported.`);
+  }
+  // [END automl_vision_import_data]
+}
+
+async 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);
+
+  // 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.`);
+  }
+  // [END automl_vision_export_data]
+}
+
+async 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.`);
+  }
+  // [END automl_vision_delete_dataset]
+}
+
+require(`yargs`) // eslint-disable-line
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: 'us-central1',
+      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: `image_classification_dataset_metadata:*`,
+      type: `string`,
+      requiresArg: false,
+      description: `Name of the Dataset to search for`,
+    },
+    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: `string`,
+      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 "image_classification_dataset_metadata:*"`)
+  .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/automl/vision/automlVisionModel.js b/automl/vision/automlVisionModel.js
new file mode 100644
index 00000000000..492be1f312f
--- /dev/null
+++ b/automl/vision/automlVisionModel.js
@@ -0,0 +1,550 @@
+/**
+ * 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 Vision API.
+ *
+ * For more information, see the documentation at
+ * https://cloud.google.com/vision/automl/docs/
+ */
+
+`use strict`;
+
+async function createModel(
+  projectId,
+  computeRegion,
+  datasetId,
+  modelName,
+  trainBudget
+) {
+  // [START automl_vision_create_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 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`;
+  }
+
+  // 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}`);
+  // [END automl_vision_create_model]
+}
+
+async function getOperationStatus(operationFullId) {
+  // [START automl_vision_get_operation_status]
+  const automl = require(`@google-cloud/automl`).v1beta1;
+
+  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>
+
+  // Get the latest state of a long-running operation.
+  const [response] = await client.operationsClient.getOperation(
+    operationFullId
+  );
+  console.log(`Operation status: `, response);
+  // [END automl_vision_get_operation_status]
+}
+
+async function listModels(projectId, computeRegion, filter) {
+  // [START automl_vision_list_models]
+  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`);
+  });
+  // [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}`);
+  // [END automl_vision_get_model]
+}
+
+async 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);
+
+  // 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));
+  });
+  // [END automl_vision_list_model_evaluations]
+}
+
+async function getModelEvaluation(
+  projectId,
+  computeRegion,
+  modelId,
+  modelEvaluationId
+) {
+  // [START automl_vision_get_model_evaluation]
+  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 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.
+  const [response] = await client.getModelEvaluation({
+    name: modelEvaluationFullId,
+  });
+  console.log(util.inspect(response, false, null));
+  // [END automl_vision_get_model_evaluation]
+}
+
+async 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
+      );
+
+      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)
+          );
+        }
+      });
+    }
+  });
+  // [END automl_vision_display_evaluation]
+}
+
+async 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.`);
+  }
+  // [END automl_vision_delete_model]
+}
+
+require(`yargs`) // eslint-disable-line
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: 'us-central1',
+      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: `string`,
+      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 "image_classification_dataset_metadata:*"`)
+  .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/automl/vision/automlVisionPredict.js b/automl/vision/automlVisionPredict.js
new file mode 100644
index 00000000000..532a412cc9b
--- /dev/null
+++ b/automl/vision/automlVisionPredict.js
@@ -0,0 +1,136 @@
+/**
+ * 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';
+
+async function predict(
+  projectId,
+  computeRegion,
+  modelId,
+  filePath,
+  scoreThreshold
+) {
+  // [START automl_vision_predict]
+  const automl = require('@google-cloud/automl').v1beta1;
+  const fs = require('fs');
+
+  // 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"';
+
+  // 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');
+
+  const params = {};
+
+  if (scoreThreshold) {
+    params.score_threshold = scoreThreshold;
+  }
+
+  // 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}`);
+  });
+  // [END automl_vision_predict]
+}
+
+require(`yargs`) // eslint-disable-line
+  .demand(1)
+  .options({
+    computeRegion: {
+      alias: `c`,
+      type: `string`,
+      default: 'us-central1',
+      requiresArg: true,
+      description: `region name e.g. "us-central1"`,
+    },
+    filePath: {
+      alias: `f`,
+      default: `./resources/testImage.jpg`,
+      type: `string`,
+      requiresArg: true,
+      description: `local text file path of the content to be classified`,
+    },
+    modelId: {
+      alias: `i`,
+      //default: ``,
+      type: `string`,
+      requiresArg: true,
+      description: `Id of the model which will be used for text classification`,
+    },
+    projectId: {
+      alias: `z`,
+      type: `string`,
+      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/testImage.jpg" -s "0.5"`
+  )
+  .wrap(120)
+  .recommendCommands()
+  .help()
+  .strict().argv;
diff --git a/automl/vision/resources/flowerTraindata20lines.csv b/automl/vision/resources/flowerTraindata20lines.csv
new file mode 100644
index 00000000000..a0617c06e9d
--- /dev/null
+++ b/automl/vision/resources/flowerTraindata20lines.csv
@@ -0,0 +1,19 @@
+gs://nodejs-docs-samples-vcm/daisy/14354051035_1037b30421_n.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/5586977262_6b24412805_n.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/14707111433_cce08ee007.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/2488902131_3417698611_n.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/506348009_9ecff8b6ef.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/2331133004_582772d58f_m.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/4511693548_20f9bd2b9c_m.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/12601254324_3cb62c254a_m.jpg,daisy
+gs://nodejs-docs-samples-vcm/daisy/2473825306_62fd5f8785_n.jpg,daisy
+gs://nodejs-docs-samples-vcm/dandelion/18089878729_907ed2c7cd_m.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/284497199_93a01f48f6.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/3554992110_81d8c9b0bd_m.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/4558536575_d43a611bd4_n.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/15782158700_3b9bf7d33e_m.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/8754822932_948afc7cef.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/2229906591_e953785d13.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/3991962484_085ba2da94.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/4500964841_b1142b50fb_n.jpg,dandelion
+gs://nodejs-docs-samples-vcm/dandelion/7808545612_546cfca610_m.jpg,dandelion
diff --git a/automl/vision/resources/testImage.jpg b/automl/vision/resources/testImage.jpg
new file mode 100644
index 00000000000..852c4635210
Binary files /dev/null and b/automl/vision/resources/testImage.jpg differ
diff --git a/automl/vision/resources/testImage2.jpg b/automl/vision/resources/testImage2.jpg
new file mode 100644
index 00000000000..674a1aa4ea7
Binary files /dev/null and b/automl/vision/resources/testImage2.jpg differ