CloudAcademy

TensorFlow Machine Learning on the Amazon Deep Learning AMI

The hands-on lab is part of this learning path

Applying Machine Learning and AI Services on AWS

course-steps 5 certification 1 lab-steps 2

Lab Steps

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Logging in to the Amazon Web Services Console
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Forwarding a Virtual Machine Port through an SSH Tunnel
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Learning the Basics of TensorFlow
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Starting a Jupyter Notebook Server
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Creating a Neural Network in TensorFlow
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Visualizing the Learning of the Neural Network with TensorBoard
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Serving a Model with TensorFlow Serving
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Consuming the Model Served by TensorFlow Serving

Ready for the real environment experience?

DifficultyIntermediate
Duration1h
Students112

Description

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:

 

About the Author

Students8594
Labs64
Courses6
Learning paths3

Logan has been involved in software development and research for over eleven years, including six years in the cloud. He is an AWS Certified DevOps Engineer - Professional, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, and Certified Kubernetes Administrator (CKA). He earned his Ph.D. studying design automation and enjoys all things tech.