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In his bestselling first guide, Eric Siegel defined how machine studying works. Now, in The AI Playbook, he reveals the right way to capitalize on it.
“Eric Siegel delivers a strong primer on machine studying, the important thing mechanism in AI. A forward-looking, sensible guide and a must-read for anybody within the data financial system.”
—Scott Galloway, NYU Stern Professor of Advertising and marketing and bestselling writer of The 4
“An antidote to right this moment’s relentless AI hype—why some AI initiatives thrive whereas others fail and what it takes for firms and other people to succeed.”
—Charles Duhigg, writer of bestsellers The Energy of Behavior and Smarter Quicker Higher
The best device is the toughest to make use of. Machine studying is the world’s most essential general-purpose expertise—however it’s notoriously tough to launch. Exterior Huge Tech and a handful of different main firms, machine studying initiatives routinely fail to deploy, by no means realizing worth. What’s lacking? A specialised enterprise observe appropriate for broad adoption. In The AI Playbook, bestselling writer Eric Siegel presents the gold-standard, six-step observe for ushering machine studying tasks from conception to deployment. He illustrates the observe with tales of success and of failure, together with revealing case research from UPS, FICO, and distinguished dot-coms. This disciplined method serves each side: It empowers enterprise professionals, and it establishes a sorely wanted strategic framework for information professionals.
Past detailing the observe, this guide additionally upskills enterprise professionals—painlessly. It delivers an important but pleasant dose of semi-technical background information that every one stakeholders want to steer or take part in machine studying tasks, finish to finish. This places enterprise and information professionals on the identical web page in order that they will collaborate deeply, collectively establishing exactly what machine studying is known as upon to foretell, how nicely it predicts, and the way its predictions are acted upon to enhance operations. These necessities make or break every initiative—getting them proper paves the way in which for machine studying’s value-driven deployment.
A word from the writer:
What sort of AI does this guide cowl? The buzzword AI can imply many issues, however this guide is about machine studying, which is a central foundation for—and what many imply by—AI. To be particular, this guide covers probably the most important use instances of machine studying, these designed to enhance a variety of enterprise operations.
From the Writer
Q & A with Creator Eric Siegel
What is that this guide about?
This guide presents a strategic and tactical playbook for launching machine studying, a six-step self-discipline to run an ML mission in order that it efficiently deploys. I name this observe bizML.
Alongside the way in which, the guide additionally delivers the semi-technical background information everybody collaborating within the mission wants—in a pleasant, accessible approach anybody can perceive. Due to that protection, the guide additionally serves as a non-technical introduction to the sphere for newcomers.
Why does machine studying want a specialised enterprise observe?
Right here’s the issue. ML is the world’s strongest typically relevant expertise. However ML can solely enhance large-scale operations by altering them. For that cause, an ML mission shouldn’t be seen as “a expertise mission.” As a substitute, to make an impression, it should be reframed as a enterprise mission meant to enhance operational efficiency, with ML as just one part—one which’s vital however not enough.
With the eye overwhelmingly centered on the technical portion of an ML mission, the business has failed to ascertain a extensively adopted enterprise observe for finishing up the entire different half of a profitable ML mission. In consequence, new ML initiatives routinely fail to deploy.
Who is that this guide for?
This guide serves anybody who needs to realize worth with ML by collaborating in its enterprise deployment, regardless of whether or not you’ll play a task on the enterprise aspect or the technical aspect.
At the start, I wrote this guide for enterprise professionals—the individuals who run the ML mission, maintain stakes in it, make selections about it, or handle the operations that might be modified (and improved) by it. This contains executives, administrators, managers, consultants, and leaders of all types.
However this guide is for techies, too. If you happen to’re a knowledge scientist, ML engineer, or any sort of technical practitioner concerned with ML, this guide invitations you to step again from the hands-on, technical work and acquire a brand new perspective on the holistic paradigm inside which you might be contributing.
What sort of AI does this guide cowl?
The buzzword AI can imply many issues, however this guide is about ML, which is a central foundation for—and what many imply by—AI. To be particular, this guide covers probably the most important use instances of machine studying, these designed to enhance a variety of enterprise operations. This guide doesn’t cowl different areas which can be additionally typically known as AI, together with synthetic normal intelligence (hypothetical methods that will be able to any mental process people can do), pure language processing, rule-based methods, and laptop imaginative and prescient.
Does this guide pertain to generative AI?
Sure. Generative AI dazzles the world by writing textual content and producing pictures—however in terms of bettering operational efficiencies, classical ML (aka predictive AI) has lengthy reigned supreme. Nonetheless, generative AI can also be nicely suited and stands to doubtlessly beat out classical ML in some arenas. The bizML observe introduced by this guide additionally serves generative AI—for tasks that apply generative AI to measurably enhance nice numbers of operational selections. For both sort of expertise, bizML will get you there, guiding the mission to a profitable deployment.
Does this guide pertain to predictive analytics?
Sure—predictive analytics is a significant subset of ML. It’s the software of ML strategies for sure enterprise issues. Alternatively, in lots of contexts, predictive analytics is solely a synonym for machine studying.
Writer : The MIT Press (February 6, 2024)
Language : English
Hardcover : 256 pages
ISBN-10 : 0262048906
ISBN-13 : 978-0262048903
Merchandise Weight : 1.05 kilos
Dimensions : 6.31 x 0.88 x 9.25 inches
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