TensorFlow Machine Learning on the Amazon Deep Learning AMI

Lab Steps

Logging in to the Amazon Web Services Console
Forwarding a Virtual Machine Port through an SSH Tunnel
Learning the Basics of TensorFlow
Starting a Jupyter Notebook Server
Creating a Neural Network in TensorFlow
Visualizing the Learning of the Neural Network with TensorBoard
Serving a Model with TensorFlow Serving
Consuming the Model Served by TensorFlow Serving
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The hands-on lab is part of these learning paths

Applying Machine Learning and AI Services on AWS
course-steps 5 certification 1 lab-steps 2

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Time Limit1h 40m
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Lab Overview

TensorFlow is a popular framework used for machine learning. The Amazon Deep Learning AMI comes bundled with everything you need to start using TensorFlow from development through to production. In this Lab, you will develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Create machine learning models in TensorFlow
  • Visualize TensorFlow graphs and the learning process in TensorBoard
  • Serve trained TensorFlow models with TensorFlow Serving
  • Create clients that consume served TensorFlow models, all with the Amazon Deep Learning AMI

Lab Prerequisites

You should be familiar with:

  • Working at the Linux command line
  • The Python programming language
  • Some linear algebra knowledge is beneficial (basic vector and matrix operations)
  • Basic understanding of neural networks is beneficial, but not required

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:



January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab


About the Author

Learning paths12

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.