diff --git a/automl/snippets/quickstart.js b/automl/snippets/quickstart.js
index 4cf2c92546a..947092919b7 100644
--- a/automl/snippets/quickstart.js
+++ b/automl/snippets/quickstart.js
@@ -1,5 +1,5 @@
 /**
- * Copyright 2018, Google, Inc.
+ * Copyright 2019, Google, Inc.
  * 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
@@ -15,7 +15,60 @@
 
 'use strict';
 
-// [START automl_quickstart]
-// TBD
-// [END automl_quickstart]
+async function main(
+  projectId,
+  computeRegion,
+  modelId,
+  filePath,
+  scoreThreshold
+) {
+  // [START automl_quickstart]
+  const automl = require('@google-cloud/automl');
+  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. “ICN723541179344731436”`;
+  // const filePath = `local text file path of content to be classified, e.g. "./resources/flower.png"`;
+  // 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_quickstart]
+}
+main(...process.argv.slice(2)).catch(err => {
+  console.error(err);
+  process.exitCode = 1;
+});