Machine Learning Engineering
Machine Learning Engineering
About the Book
From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.
This is what Cassie Kozyrkov, Chief Decision Scientist at Google, tells about the book in the Foreword:
I’d like to let you in on a secret: when people say ‘machine learning’ it sounds like there’s only one discipline here. Surprise! There are actually two machine learnings, and they are as different as innovating in food recipes and inventing new kitchen appliances. Both are noble callings, as long as you don’t get them confused; imagine hiring a pastry chef to build you an oven or an electrical engineer to bake bread for you!
The bad news is that almost everyone does mix these two machine learnings up. No wonder so many businesses fail at machine learning as a result. What no one seems to tell beginners is that most machine learning courses and textbooks are about Machine Learning Research - how to build ovens (and microwaves, blenders, toasters, kettles… the kitchen sink!) from scratch, not how to cook things and innovate with recipes at enormous scale. In other words, if you’re looking for opportunities to create innovative ML-based solutions to business problems, you want the discipline called Applied Machine Learning, not Machine Learning Research, so most books won’t suit your needs.
And now for the good news! You’re looking at one of the few true Applied Machine Learning books out there. That’s right, you found one! A real applied needle in the haystack of research-oriented stuff. Excellent job, dear reader… unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won’t be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book. This one is different.
When I created Making Friends with Machine Learning in 2016, Google’s Applied Machine Learning course loved by more than ten thousand of our engineers and leaders, I gave it a very similar structure to the one in this book. That’s because doing things in the right order is crucial in the applied space. As you use your newfound data powers, tackling certain steps before you’ve completed others can lead to anything from wasted effort to a project-demolishing kablooie. In fact, the similarity in table of contents between this book and my course is what originally convinced me to give this book a read. In a clear case of convergent evolution, I saw in the author a fellow thinker kept up at night by the lack of available resources on Applied Machine Learning, one of the most potentially-useful yet horribly-misunderstood areas of engineering, enough to want to do something about it. So, if you’re about to close this book, how about you do me a quick favor and at least ponder why the Table of Contents is arranged the way it is. You’ll learn something good just from that, I promise.
So, what’s in the rest of the book? The machine learning equivalent of a bumper guide to innovating in recipes to make food at scale. Since you haven’t read the book yet, I’ll put it in culinary terms: you’ll need to figure out what’s worth cooking / what the objectives are (decision-making and product management), understand the suppliers and the customers (domain expertise and business acumen), how to process ingredients at scale (data engineering and analysis), how to try many different ingredient-appliance combinations quickly to generate potential recipes (prototype phase ML engineering), how to check that the quality of the recipe is good enough to serve (statistics), how to turn a potential recipe into millions of dishes served efficiently (production phase ML engineering), and how to ensure that your dishes stay top-notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered (reliability engineering). This book is one of the few to offer perspectives on each step of the end-to-end process.
Now would be a good moment for me to be blunt with you, dear reader. This book is pretty good. It is. Really. But it’s not perfect. It cuts corners on occasion - just like a professional machine learning engineer is wont to do - though on the whole, it gets its message right. And, since it covers an area with rapidly-evolving best practices, it doesn’t pretend to offer the last word on the subject. But even if it were terribly sloppy, it would still be worth reading. Given how few comprehensive guides to Applied Machine Learning are out there, a coherent introduction to these topics is worth its weight in gold. I’m so glad this one is here!
One of my favorite things about this book is how fully it embraces the most important thing you need to know about machine learning: mistakes are possible... and sometimes they hurt. As my colleagues in site reliability engineering love to say, “Hope is not a strategy.” Hoping that there will be no mistakes is the worst approach you can take. This book does so much better. It promptly shatters any false sense of security you were tempted to have about building an AI system that is more “intelligent” than you are. (Um, no. Just no.) Then it diligently takes you through a survey of all kinds of things that can go wrong in practice and how to prevent/detect/handle them. This book does a great job of outlining the importance of monitoring, how to approach model maintenance, what to do when things go wrong, how to think about fallback strategies for the kinds of mistakes you can't anticipate, how to deal with adversaries who try to exploit your system, and how to manage the expectations of your human users (there’s also a section on what to do when your, er, users are machines). These are hugely important topics in practical machine learning, but they’re so often neglected in other books. Not here.
If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book.
The Leanpub 45-day 100% Happiness Guarantee
Within 45 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
See full terms
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers), EPUB (for phones and tablets) and MOBI (for Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Production HaskellMatt Parsons
Are you excited about Haskell, but don't know where to begin? Are you thrilled by the technical advantages, but worried about the unknown pitfalls? This book has you covered.
Functional Design and ArchitectureAlexander Granin
Software Design in Functional Programming, Design Patterns and Practices, Methodologies and Application Architectures. How to build real software in Haskell with less efforts and low risks. The first complete source of knowledge.
Ansible for KubernetesJeff Geerling
Ansible is a powerful infrastructure automation tool. Kubernetes is a powerful application deployment platform. Learn how to use these tools to automate massively-scalable, highly-available infrastructure.
Ansible for DevOpsJeff Geerling
Ansible is a simple, but powerful, server and configuration management tool. Learn to use Ansible effectively, whether you manage one server—or thousands.
Composing SoftwareEric Elliott
All software design is composition: the act of breaking complex problems down into smaller problems and composing those solutions. Most developers have a limited understanding of compositional techniques. It's time for that to change.
Practical FP in Scala: A hands-on approachGabriel Volpe
A practical book aimed for those familiar with functional programming in Scala who are yet not confident about architecting an application from scratch.
Together, we will develop a purely functional application using the best libraries in the Cats ecosystem, while learning about design patterns and best practices.
Tame your Work FlowSteve Tendon and Daniel Doiron
Do you need a high performance enterprise governance approach improving management, execution and delivery while dealing with multiple projects/products, events, stakeholders and teams? Giving you better bottom line results, faster time to market, less work, better predictability, happier employees, and delighted clients? Then learn about TameFlow!
Invest In Digital Health - The Medical Futurist's GuideDr. Bertalan Mesko
Artificial Intelligence and Digital Health are booming. In this book, we explain why now it's a good time to invest in Digital Health and give recommendations on where to invest by looking at the top 24 technological trends we find the most promising.
The Hundred-Page Machine Learning BookAndriy Burkov
Everything you really need to know in Machine Learning in a hundred pages.
Windows 10 System Programming, Part 1Pavel Yosifovich
Growing Agile: The Complete Coach's Guide
7 BooksGrowing Agile: Coach's Guide Series This bundle provides a collection of training and workshop plans for a variety of agile topics. The series is aimed at agile coaches, trainers and ScrumMasters who often find themselves needing to help teams understand agile concepts. Each book in the series provides the plans, slides, handouts and activity...
The Tester's Library
8 BooksThe Tester's Library consists of eight five-star books that every software tester should read and re-read. As bound books, this collection would cost over $200. Even as e-books, their price would exceed $80, but in this bundle, their cost is only $49.99. Here are the books, and why they should be in your library: Perfect Software and Other...
11 BooksIn this bundle, you will find 10 different agile books. They are about different aspects of being agile. - finding a job - doing coding dojo's - Retrospectives - Personal kanban - a non-typical coaching book and even a book that gives you an insight in the lives of some agile people.
WTFlop 6M + HU - Beta Bundle
Growing Agile: Coach's Guide Series
4 BooksThis bundle provides a collection of training and workshop plans for a variety of agile topics. The series is aimed at agile coaches, trainers and ScrumMasters who often find themselves needing to help teams understand agile concepts. Each book in the series provides the plans, slides, handouts and activity instructions to run a number of...
Marionette.js A to Z
Build A Better Backbone App
3 BooksThe best way to learn new development skills is through experience, but that takes time you don't have.Get the best of both worlds with this bundle: you'll learn how to produce modern web applications by learning from experienced developers like Derick Bailey and David Sulc. BackboneJS is one of the favorite tools on the web today, but it...
General Systems Thinker Bundle
5 BooksThe General Systems Thinker Bundle is just that: a bundle of five books to advance the reader one giant step toward improved thinking, based on General Systems principles. Four of the books are the complete General Systems Series. The fifth is fictional piece which shows some general systems thinkers in action. It's a mystery in which a group of...
Experiential Learning Bundle
4 BooksThis bundle provides all four volumes of the popular Experiential Learning Series at a savings of $20 over the price if purchased separately.
2 BooksAfter getting up and running with Ansible in Jeff Geerling's Ansible for DevOps, strengthen your skills managing tens to thousands of instances and services in Amazon's AWS cloud with Yan Kurniawan's Ansible for AWS.