دانلود کتاب بازی هوش مصنوعی – تسلط بر هنر نادر استقرار یادگیری ماشین

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. 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 and its execution, the industry has failed to establish a widely adopted business practice for executing the whole other half of a successful ML project. As a result, new ML initiatives routinely fail to deploy. Not at all. Many ML projects succeed, even if it’s only a minority—even a fraction of this popular field’s many projects is still many. Moreover, in certain circumstances an ML project is bound to succeed, such as highpriority projects at a Big Tech firm or projects meant to update an existing model that’s already deployed. The industrial world remains bullish on ML because its great potential remains intact. No. When an ML project follows bizML, the organizational practice presented by this book, everyone involved in the project participates in that practice in some way. Only with universal familiarity with this end-to-end practice—and with the semi-technical background knowledge that drives it —can the team collaborate most effectively. 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 execution and gain a new perspective on the holistic paradigm within which you are contributing.