Rustem Feyzkhanov - Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Amey Varangaonkar
Acquisition Editor: Nelson Morris
Content Development Editor: Ronnel Mathew
Technical Editor: Dinesh Pawar
Copy Editor: Safis Editing
Project Coordinator: Hardik Bhinde
Proofreader: Safis Editing
Indexer: Rekha Nair
Graphics: Tom Scaria
Production Coordinator: Jyoti Chauhan

First published: January 2019

Production reference: 1310119

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-83855-160-5

www.packtpub.com

Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.

• Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals

• Improve your learning with Skill Plans built especially for you

• Get a free eBook or video every month

• Mapt is fully searchable

• Copy and paste, print, and bookmark content

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at customercare@packtpub.com for more details.

At www.packt.com , you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.

Rustem Feyzkhanov is a machine learning engineer at Instrumental. He works on creating analytical models for the manufacturing industry. He is also passionate about serverless infrastructures and AI deployment. He has ported several packages on AWS Lambda, ranging from TensorFlow/Keras/sklearn for machine learning to PhantomJS/Selenium/WRK for web scraping. One of these apps was featured on the AWS serverless repository's home page.

If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.

This book prepares you to use your own custom-trained models with AWS Lambda to achieve a simplified serverless computing approach without spending much time and money. By the end of the book, you will be able to implement a project that demonstrates the use of AWS Lambda in serving TensorFlow models.

Furthermore, we will cover deep learning and the TensorFlow framework. We will look at how to train neural networks, but more importantly, we will cover how to use pretrained neural networks in your application and where you can find them. After that, we will look into how to deploy deep learning applications using the serverless approach. We will also cover their advantages, disadvantages, possible limitations, and good practices.

Later on, we will build a number of applications that will utilize the serverless deep learning approach. We will make a planning API and take a look at the AWS API Gateway service, and explore how to deploy everything in a convenient way. In the later stages, we will make a deep learning pipeline and an AWS simple query service. We will explore how to use it with AWS Lambda and showcase how to deploy the application.

This course will benefit data scientists who want to learn how to deploy models easily, and beginners who want to learn about deploying into the cloud. No prior knowledge of TensorFlow or AWS is required.

, Beginning with Serverless Computing and AWS Lambda , goes through all the examples that we are planning to look at. We will also describe the serverless concept and how it has changed the current cloud infrastructure environment. Finally, we will see how serverless deep learning allows us to make projects that are much easier to implement than traditional deployment techniques, while being just as scalable and cheap.

, Starting Deployment with AWS Lambda Functions, introduces AWS Lambda and explains how to create an AWS account. We will make our first Lambda and see how to easily deploy it using the Serverless Framework.

, Deploying TensorFlow Models, introduces the TensorFlow framework, along with several examples on how to train and export a model. Also, we will look at the repositories of pretrained models that anyone can use for their own tasks. Finally, we will show how to import pretrained models that is required for your projects.

, Working with TensorFlow on AWS Lambda, digs into the specifics of how to start working with serverless TensorFlow. Also, we will see small details of how serverless TensorFlow is different from traditional deployment from the perspective of cost, scale, and speed. We will also look at how to start with using the standard AWS UI, and then understand how the same can be done using the Serverless Framework.

, Creating the Deep Learning API, explains how to make a deep learning REST API . Then we will introduce AWS API Gateway and learn how to make the application using two methods: AWS UI and the Serverless Framework.