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The AI Playbook

Mastering the Rare Art of Machine Learning Deployment (Management on the Cutting Edge)

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The AI Playbook

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

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 initiatives, 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 predictive AI 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 predictive AI's value-driven deployment.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2024 Eric Siegel (P)2024 Eric Siegel
Ciencia de Datos Gestión Gestión y Liderazgo Historia y Cultura Informática Tecnología y Sociedad Negocio Tecnología Aprendizaje automático Inteligencia artificial Liderazgo
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Reseñas de la Crítica

"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

“There's a big difference between theory and practice. This is the guide that tells you how to really make data come alive through machine learning.”

—DJ Patil, General Partner, Great Point Ventures and Former U.S. Chief Data Scientist

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Every data scientist, data engineer, and businessperson should read this book. Its framework is not only easy to understand but frankly makes sense in the AI-hype era. Full of examples and analogies, the book conveys its clear message from the get-go: to successfully deploy machine learning in your business, you have to sell it as a business solution, not a technology solution.

The most important book on AI

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As above. It seems like good qual for author’s consulting business.
Great topic. But needs crisper thinking and writing. Sounds like a great guy to hire.

Good topic, very average thinking, very average writing

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Have you ever had a boring professor? The reader of this sounds like it. This book is one that is too thick in the weeds, not of entrepreneur mindset and overall I wish that they led with the optional faq chapter as the sample read. I would not have purchased it. This author obviously knows his stuff but this book is definitely not geared towards business owners.

I feel misled by the sample...

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This book seems to be for non technical people who have been also living under a rock and never read a blog post or have any understanding of product development or ML. I have no idea what the audience would be.

Basic book not worth time

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