Författare
Eric Siegel
Eric Siegel är en uppskattad författare inom Ekonomi och Ledarskap med totalt 2 böcker tillgängliga på Bokkollen, utgivna hos MIT Press Ltd, John Wiley & Sons Inc.
Bland verken finns AI Playbook, som toppar listan över Eric Siegels populäraste böcker. Verken spänner över ekonomi & ledarskap och tilltalar läsare som uppskattar genren.
Letar du efter något nytt att läsa? Prova Predictive Analytics – ett annat uppskattat verk av Eric Siegel.
På Bokkollen gör vi det enkelt att navigera i Eric Siegels författarskap. Vår databas uppdateras ständigt med nya släpp och format, så oavsett om du söker efter en lättläst pocket för semestern, en lyxig inbunden presentutgåva eller en digital ljudbok för pendlingen, har vi rätt utgåva för dig.
Jämför snabbt och smidigt priser på alla böcker av Eric Siegel hos Sveriges ledande bokhandlare – som Adlibris, Bokus och Akademibokhandeln – och hitta alltid det bästa erbjudandet utan att betala för mycket.
AI Playbook
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.
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