Embedded AI: Intelligence at the Deep Edge

Putting AI on a small device is not just a matter of training a model and loading it onto a board. The work also involves sensors, noisy data, hardware limits, and making the whole system function together. Embedded AI: Intelligence at the Deep Edge follows that engineering path, connecting machine-learning foundations with practical embedded projects.

David Such begins with embedded systems and applied machine learning, then moves through sensor data and into complete builds. The result is a hands-on look at how intelligent behavior can be designed for devices operating close to the data they collect.

Build a foundation before the first project

The opening section introduces embedded systems, applied machine learning, deep learning, and exploratory data analysis. Together, these topics establish the concepts needed to understand how models and embedded hardware fit into a working design.

Make sensor data useful

Later chapters focus on smart sensors, inertial measurement unit (IMU) data preprocessing, and sensor fusion. These subjects bring attention to the information feeding an AI system: preparing and combining sensor readings is part of building a dependable embedded application, not an afterthought.

Follow the ideas into complete builds

The project-focused final section takes on sensor machine learning, real-time audio noise suppression, an AI MIDI synthesizer, hot-word detection, and an Arduino battery monitor and logging shield. The range makes the book especially interesting to readers who want to see embedded AI applied across sensing, audio, music, and device monitoring.

Who may find it useful

Embedded developers can use the book to explore machine learning in the context of hardware and sensors. Readers coming from machine learning may appreciate the shift toward real devices and system-level concerns, while technically minded makers can follow the progression from fundamentals to substantial projects.

For readers curious about AI beyond the desktop or cloud, this book offers a grounded route into intelligence at the deep edge—where models meet real sensors, boards, and engineering constraints.