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Cloud ML Engine: Qwik Start

60m access · 60m completion
Student Resources
  • What is Machine Learning?
  • Harness the Power of Machine Learning with Cloud ML Engine
  • Cloud ML Engine: Qwik Start - Qwiklabs Preview
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1 Credit

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This lab costs 1 Credit to run. You can purchase credits or a subscription under My Account.

Cloud ML Engine: Qwik Start

GSP076

Google Cloud Self-Paced Labs

Overview

This lab will give you hands-on practice with TensorFlow model training, both locally and on Cloud ML Engine. After training, you will learn how to deploy your model to Cloud ML Engine for serving (prediction). You'll train your model to predict income category of a person using the United States Census Income Dataset.

What you will use:

In this lab, you will get hands-on practice with the following tools and services:

  • TensorFlow, which is an open source library for numerical computation, specializing in machine learning applications.
  • Cloud Machine Learning Engine, which brings the power and flexibility of TensorFlow to the cloud, letting you perform large scale training on a managed cluster, and then scalably server your trained model for prediction.
  • To build your model, you'll use TensorFlow's prebuilt DNNCombinedLinearClassifier model is sometimes called ‘Wide & Deep'

What you will learn:

  • How to build a TensorFlow model, and how to train it both locally and in the cloud using Cloud ML Engine (CMLE).
  • How to use your trained model for prediction, and how to use CMLE's online prediction service.

What you need:

  • A basic familiarity with Python and Linux commands will be helpful, but not necessary.

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Score

—/25

Set up a Google Cloud Storage bucket

Run Step

/ 5

Upload the data files to your Cloud Storage bucket

Run Step

/ 5

Run a single-instance trainer in the cloud

Run Step

/ 5

Create a Cloud ML Engine model

Run Step

/ 5

Create a version v1 of your model

Run Step

/ 5

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