Why does machine learning need a specialized business practice?

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 of an ML project, the industry has failed to establish a widely adopted business practice for carrying out the whole other half of a successful ML project. As a result, new ML initiatives routinely fail to deploy.

Who is this book for?

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, technical work and gain a new perspective on the holistic paradigm within which you are contributing.

What kind of AI does this book cover?

The buzzword AI can mean many things, but this book is about ML, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations. This book does not cover other areas that are also sometimes referred to as AI, including artificial general intelligence (hypothetical systems that would be capable of any intellectual task humans can do), natural language processing, rule-based systems, and computer vision.

Does this book pertain to generative AI?

Yes. Generative AI dazzles the world by writing text and producing images—but when it comes to improving operational efficiencies, classical ML (aka predictive AI) has long reigned supreme. However, generative AI is also well suited and stands to potentially beat out classical ML in some arenas. The bizML practice presented by this book also serves generative AI—for projects that apply generative AI to measurably improve great numbers of operational decisions. For either kind of technology, bizML gets you there, guiding the project to a successful deployment.

Does this book pertain to predictive analytics?

Yes—predictive analytics is a major subset of ML. It is the application of ML methods for certain business problems. Alternatively, in many contexts, predictive analytics is simply a synonym for machine learning.