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

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

Ready for the real environment experience?

DifficultyIntermediate
Duration1h
Students120

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

Students10222
Labs68
Courses7
Learning paths4

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, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Linux Foundation Certified System Administrator (LFCS). He earned his Ph.D. studying design automation and enjoys all things tech.