AI at the Edge

Foreword

In 2022, GitHub CEO Thomas Dohmke said, “I think the shift to the cloud will happen at such a rapid rate, that in just a few years I predict there will be no more code on your local computer.”¹ This book does a great job of explaining why I and a lot of other people in the emerging field of edge ML think he’s dead wrong.

We’re starting to see the emergence of many practical applications like high-quality voice recognition, forest fire prevention, and smart home controls that are only possible because local devices are now capable of running advanced machine learning algorithms. Jenny and Dan have put together a wonderful book that not only explains why adding intelligence to edge applications is so crucial to solving important problems, but also walks the reader through the steps required to design, implement, and test these kinds of applications.

It can feel pretty intimidating when you first start looking at a machine learning project on the edge. The field involves a lot of jargon, is changing rapidly, and requires knowledge from domains like embedded systems and artificial intelligence that have traditionally not been well integrated. What the authors have achieved is a gentle but thorough introduction to everything you need to know to work effectively on an application. They’ve also managed to make it accessible to a wide range of readers thanks to their emphasis on examples from the real world and use of plain English instead of math or code to explain even complex ...