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The AI Playbook, with a new preface by the author (Mastering the Rare Art of Machine Learning Deployment) - 9780262057240
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$22.95
| Expected release date is Oct 27th 2026 |
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Product Details
Author:
Eric Siegel
Format:
Paperback
Pages:
256
Publisher:
MIT Press (October 27, 2026)
Imprint:
The MIT Press
Release Date:
October 27, 2026
Language:
English
Audience:
General/trade
ISBN-13:
9780262057240
ISBN-10:
0262057247
Weight:
13oz
Dimensions:
6" x 9"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260405T164652_155746768-20260405.xml
Folder:
RandomHouse
List Price:
$22.95
Country of Origin:
United States
Pub Discount:
65
Series:
Management on the Cutting Edge
Case Pack:
24
As low as:
$17.67
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Overview
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; 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, aka predictive AI 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:
The buzzword AI can mean many things, but this book is about the most vital use cases of machine learning, those designed to improve large-scale business operations—aka predictive AI or predictive analytics.
“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; 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, aka predictive AI 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:
The buzzword AI can mean many things, but this book is about the most vital use cases of machine learning, those designed to improve large-scale business operations—aka predictive AI or predictive analytics.
