I’m excited to announce a new book, AI at the Edge, written by myself and Jenny Plunkett!
The book is currently available as an Early Release for O’Reilly Online subscribers. Our first chapter is already published, and we’re aiming to publish one chapter per month until the whole thing is done—at which point it will be available for regular purchase.
Since Pete Warden and I wrote our TinyML book, embedded machine learning has grown from an intriguing little field into a key technology that is transforming the way we use sensor data across industry, consumer tech, and research. The TinyML Summit—which in 2019 we held in a meeting room on the Google campus—is now a large international conference, and TinyML is the subject of a popular Harvard course.
With TinyML and other edge AI technologies reaching maturity, engineering professionals and product managers who wish to use them need a practical guide to solving real-world industrial, commercial, and scientific problems. AI at the Edge is designed to go beyond the fundamentals and help readers understand the edge AI landscape, evaluate potential solutions, design successful applications, and support them in the field—while avoiding the unique risks presented by AI engineering.
Description: AI at the Edge
Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (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 flexible embedded Linux devices—for applications that reduce latency, protect privacy, and work without a network connection, greatly expanding the capabilities of the IoT.
This practical guide gives engineering professionals and product managers 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 roadmap will help you get started.
- Develop your expertise in artificial intelligence and machine learning on edge devices
- Understand which projects are best solved with edge AI
- Explore typical design patterns used with edge AI apps
- Use an iterative workflow to develop an edge AI application
- Optimize models for deployment to embedded devices
- Improve model performance based on feedback from real-world use