The AI Playbook: Mastering the Rare Art of Machine Learning Deployment (Management on the Cutting Edge)

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.

“Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.”
—Scott Galloway, NYU Stern Professor of Marketing and bestselling author of The Four

“An antidote to today’s relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.”
—Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better

The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology—but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals, and it establishes a sorely needed strategic framework for data professionals.

Beyond detailing the practice, this book also upskills business professionals—painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative—getting them right paves the way for machine learning’s value-driven deployment.

A note from the author:

What kind of AI does this book cover? The buzzword AI can mean many things, but this book is about machine learning, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations.

From the Publisher

Q & A with Author Eric Siegel

What is this book about?

This book presents a strategic and tactical playbook for launching machine learning, a six-step discipline to run an ML project so that it successfully deploys. I call this practice bizML.

Along the way, the book also delivers the semi-technical background knowledge everyone participating in the project needs—in a friendly, accessible way anyone can understand. Because of that coverage, the book also serves as a non-technical introduction to the field for newcomers.

Why does machine learning need a specialized business practice?

Here’s the problem. ML is the world’s most powerful generally applicable technology. But ML can only improve large-scale operations by changing them. For that reason, an ML project shouldn’t be viewed as “a technology project.” Instead, to make an impact, it must be reframed as a business project meant to improve operational performance, with ML as only one component—one that’s necessary but not sufficient.

With the attention overwhelmingly focused on the technical portion of an ML project, the industry has failed to establish a widely adopted business practice for carrying out the whole other half of a successful ML project. As a result, new ML initiatives routinely fail to deploy.

Who is this book for?

This book serves anyone who wishes to gain value with ML by participating in its business deployment, no matter whether you’ll play a role on the business side or the technical side.

First and foremost, I wrote this book for business professionals—the people who run the ML project, hold stakes in it, make decisions about it, or manage the operations that will be changed (and improved) by it. This includes executives, directors, managers, consultants, and leaders of all kinds.

But this book is for techies, too. If you’re a data scientist, ML engineer, or any kind of technical practitioner involved with ML, this book invites you to step back from the hands-on, technical work and gain a new perspective on the holistic paradigm within which you are contributing.

What kind of AI does this book cover?

The buzzword AI can mean many things, but this book is about ML, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations. This book does not cover other areas that are also sometimes referred to as AI, including artificial general intelligence (hypothetical systems that would be capable of any intellectual task humans can do), natural language processing, rule-based systems, and computer vision.

Does this book pertain to generative AI?

Yes. Generative AI dazzles the world by writing text and producing images—but when it comes to improving operational efficiencies, classical ML (aka predictive AI) has long reigned supreme. However, generative AI is also well suited and stands to potentially beat out classical ML in some arenas. The bizML practice presented by this book also serves generative AI—for projects that apply generative AI to measurably improve great numbers of operational decisions. For either kind of technology, bizML gets you there, guiding the project to a successful deployment.

Does this book pertain to predictive analytics?

Yes—predictive analytics is a major subset of ML. It is the application of ML methods for certain business problems. Alternatively, in many contexts, predictive analytics is simply a synonym for machine learning.

ASIN ‏ : ‎ B0C4J7TG5D
Publisher ‏ : ‎ The MIT Press (February 6, 2024)
Publication date ‏ : ‎ February 6, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 1.6 MB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 253 pages