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Andrew P. McMahon - Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

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Andrew P. McMahon Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
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Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments

Key Features
  • Explore hyperparameter optimization and model management tools
  • Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
  • Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases
Book Description

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.

Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. Youll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, youll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, youll work through examples to help you solve typical business problems.

By the end of this book, youll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

What you will learn
  • Find out what an effective ML engineering process looks like
  • Uncover options for automating training and deployment and learn how to use them
  • Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
  • Understand what aspects of software engineering you can bring to machine learning
  • Gain insights into adapting software engineering for machine learning using appropriate cloud technologies
  • Perform hyperparameter tuning in a relatively automated way
Who this book is for

This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If youre someone who manages or wants to understand the production life cycle of these systems, youll find this book useful. Intermediate-level knowledge of Python is necessary.

Table of Contents
  1. Introduction to ML Engineering
  2. The Machine Learning Development Process
  3. From Model to Model Factory
  4. Packaging Up
  5. Deployment Patterns and Tools
  6. Scaling Up
  7. Building an Example ML Microservice
  8. Building an Extract Transform Machine Learning Use Case

Andrew P. McMahon: author's other books


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Andrew P. McMahon - Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Andrew P. McMahon
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

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Machine Learning Engineering with Python Manage the production life cycle of - photo 1
Machine Learning Engineering with Python

Manage the production life cycle of machine learning models using MLOps with practical examples

Andrew P. McMahon

BIRMINGHAMMUMBAI Machine Learning Engineering with Python Copyright 2021 Packt - photo 2

BIRMINGHAMMUMBAI

Machine Learning Engineering with Python

Copyright 2021 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.

Publishing Product Manager: Ali Abidi

Senior Editor: David Sugarman

Content Development Editor: Nathanya Dias

Technical Editor: Sonam Pandey

Copy Editor: Safis Editing

Project Coordinator: Aparna Ravikumar Nair

Proofreader: Safis Editing

Indexer: Sejal Dsilva

Production Designer: Jyoti Chauhan

First published: November 2021

Production reference: 1280921

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80107-925-9

www.packt.com

Contributors
About the author

Andrew Peter (Andy) McMahon is a machine learning engineer and data scientist with experience of working in, and leading, successful analytics and software teams. His expertise centers on building production-grade ML systems that can deliver value at scale. He is currently ML Engineering Lead at NatWest Group and was previously Analytics Team Lead at Aggreko.

He has an undergraduate degree in theoretical physics from the University of Glasgow, as well as master's and Ph.D. degrees in condensed matter physics from Imperial College London. In 2019, Andy was named Data Scientist of the Year at the International Data Science Awards. He currently co-hosts the AI Right podcast, discussing hot topics in AI with other members of the Scottish tech scene.

This book, and everything I've ever achieved, would not have been possible without a lot of people. I wish to thank my mum, for introducing me to science and science fiction, my dad, for teaching me not to have any regrets, and my wider family and friends for making my life full of laughter. Most of all, I want to thank my wife, Hayley, and my son, Teddy, for being my sunshine every single day and giving me a reason to keep pushing myself to be the best I can be.

About the reviewers

Daksh Trehan began his career as a data analyst. His love for data and statistics is unimaginable. Various statistical techniques introduced him to the world of ML and data science. While his focus is on being a data analyst, he loves to forecast given data using ML techniques. He understands the power of data in today's world and constantly tries to change the world using various ML techniques and his concrete data visualization skills. He loves to write articles on ML and AI, and these have bagged him more than 100,000 views to date. He has also contributed as an ML consultant to 365 Days as a TikTok creator, written by Dr. Markus Rach, which is available publicly on the Amazon e-book store.

Ved Prakash Upadhyay is an experienced machine learning professional. He did his master's in information science at the University of Illinois Urbana-Champaign. Currently, he is working at IQVIA as a senior machine learning engineer. His work focuses on building recommendation systems for various pharma clients of IQVIA. He has strong experience with productionalizing machine learning pipelines and is skilled with the different tools that are used in the industry. Furthermore, he has acquired an in-depth conceptual knowledge of machine learning algorithms. IQVIA is a leading global provider of advanced analytics, technology solutions, and clinical research services to the life sciences industry.

Michael Petrey is a data scientist with a background in education and consulting. He holds a master's in analytics from Georgia Tech and loves using data visualization and analysis to get people the best tools for their jobs. You might find Michael on a hike near Atlanta, eating ice cream in Boston, or at a caf in Wellington.

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