No Starch Press to Release 'Embedded AI' Guide for Resource-Constrained Hardware
David Such's 600-page manual provides a structured path for developers to deploy machine learning on the deep edge.
No Starch Press has announced the upcoming release of "Embedded AI: Intelligence at the Deep Edge," a comprehensive guide authored by veteran engineer David Such. Scheduled for publication in September 2026, the book equips embedded developers and makers with the skills to integrate machine learning into hardware with limited computational resources.
The 600-page volume focuses on practical application, featuring more than 25 hands-on projects. These include the development of a wake-word detector, a real-time AI noise suppressor, and an AI-powered MIDI synthesizer. To complete the curriculum, readers will need common hardware such as the Arduino UNO and Raspberry Pi Pico, alongside software tools including Python with TensorFlow and the Raspberry Pi Pico SDK.
The Shift to the Deep Edge
This release arrives as "Edge AI" becomes a critical priority for the Internet of Things (IoT) sector. By moving intelligence from centralized cloud servers to the local device—the "deep edge"—developers can significantly reduce latency and minimize the bandwidth required for data transmission. No Starch Press has established a reputation for high-quality technical publishing that resonates with the developer community, positioning this book as a vetted resource for the field.
Bridging the Engineering Gap
Deploying intelligence on the deep edge presents a steep technical challenge: bridging the divide between high-level machine learning, which typically relies on powerful GPUs and cloud infrastructure, and low-level embedded engineering. David Such, the founder of Reefwing Software with over 30 years of experience in embedded systems, designed the book to provide a structured engineering path. This approach allows developers to implement sophisticated AI functionality without requiring extensive prior expertise in machine learning theory.
Future Implications
As hardware capabilities evolve, the ability to run local AI will likely become a standard requirement for IoT device architecture. The industry is moving toward a model where devices can process complex sensory data locally and securely. While the book provides the foundational projects and tools, the broader trend suggests a continuing push toward autonomous, offline intelligence in consumer and industrial electronics. Developers will be watching to see how these techniques scale as newer, more efficient AI models are optimized for the microcontrollers highlighted in Such's work.