AI at the Edge: Solving Real-World Problems with Embedded Machine Learning
by Daniel Situnayake(Author), Jenny Plunkett(Author)
Publisher finelybook 出版社: O’Reilly Media; (February 14, 2023)
Language 语言: English
Print Length 页数: 512 pages
ISBN-10: 1098120205
ISBN-13: 9781098120207
Book Description
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target–from ultra-low power microcontrollers to embedded Linux devices.
This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You’ll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started.
Develop your expertise in AI and ML for edge devices
Understand which projects are best solved with edge AI
Explore key design patterns for edge AI apps
Learn an iterative workflow for developing AI systems
Build a team with the skills to solve real-world problems
Follow a responsible AI process to create effective products
From the Preface
Over the past few years, a growing community of engineers and researchers have quietly rewritten the rules for how computers interact with the physical world. The result, a technology known as “edge artificial intelligence,” promises to upend a century of computer history and touch the lives of every human being.
With a tiny software update, edge AI technology can grant cheap, energy-efficient processors—already inside everything from dishwashers to thermostats—the ability to perceive and understand the world. We can empower everyday objects with their own intelligence, no longer dependent on data-hungry centralized servers. And next-generation tools put this magic in reach of everyone, from high school students to conservation researchers.
There are already many edge AI products out there in the world. Here are some that we’ll meet in the pages of this book:
Smart devices that help prevent forest fires caused by electricity transmission, by mounting to electricity pylons and predicting when a fault may occur
Wearable bands that keep firefighters safe by warning when they’re at risk from heat strain and overexertion
Voice user interfaces that provide hands-free control of technology, no internet connection required
Smart collars that monitor the movements of wild elephants, helping researchers understand their behavior and protect them from conflict
Wildlife cameras that identify specific animal species and help scientists understand their behavior
The technology of edge AI is still fresh and new, and these existing applications are just a glimpse of what is possible. As more people learn how to work with edge AI, they’ll create applications that solve problems across every avenue of human activity.
The goal of this book is to empower you to be one of them. We want to help you create successful edge AI products based on your own unique perspectives.
AI at the Edge
About This Book
This book is designed for the engineers, scientists, product managers, and decision makers who will drive this revolution. It’s a high-level guide to the entire space, providing a workflow and a framework for solving real-world problems using edge AI.
In the first part of the book, the initial chapters will introduce and discuss the key concepts, helping you understand the lay of the land. The next few will take you through the practical processes that will help you design and implement your own applications.
In the second part of the book, starting in Chapter 11, we’ll use three end-to-end walkthroughs to demonstrate how to apply your knowledge to solve real problems in scientific, industrial, and consumer projects.
By the end of the book, you’ll feel confident in viewing the world through the lens of edge AI, and you’ll have a solid set of tools you can use to help build effective solutions.
Among other things, we hope to teach you:
The opportunities, limitations, and risks inherent to various edge AI technologies
A framework for analyzing problems and designing solutions using AI and embedded machine learning
An end-to-end practical workflow for successfully developing edge AI applications
Review
AI at the Edge introduces the new and fast-growing field of edge AI in a practical, easy-to-follow way. It demystifies jargon and highlights real challenges that you are likely to encounter when building edge AI applications. The book offers an essential guide to going from concept to deployment—a must-read for getting started in the field.
—Wiebke Hutiri, Delft University of Technology
I really love the writing style which makes complex technical topics approachable and digestible. I can imagine it being used as a reference book, returning to it time and time again—which I will certainly be doing!
—Fran Baker, Director of Sustainability and Social Impact, Arm
What a wonderfully accessible and thorough introduction to the emerging field of edge AI! It covers an impressive breadth of topics, from the core concepts to the latest hardware and software tools, it’s full of actionable advice, and includes several end-to-end examples. Anyone joining this exciting new field will benefit from the deep insights and clarity of thought this book provides.
—Aurélien Geron, former lead of YouTube’s automatic video classification team and best-selling author
This is the guide to creating smarter devices: AI at the Edge provides an excellent introduction on combining modern AI techniques and embedded systems.
—Elecia White, author of Making Embedded Systems and host of the Embedded podcast
About the Author
Daniel Situnayake is Head of Machine Learning at Edge Impulse, where he leads embedded machine learning R&D. He’s coauthor of the O’Reilly book TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, the standard textbook on embedded machine learning, and has delivered guest lectures at Harvard, UC Berkeley, and UNIFEI. Dan previously worked on TensorFlow Lite at google, and co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.
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