You already know how to build embedded systems. Now it’s time to make them intelligent. Adding AI to an embedded device takes more than training a model. You must choose the right hardware, prepare data, deploy models to resource-constrained devices, and integrate everything reliably. Drawing on over 30 years of engineering experience, David Such guides you through the complete process. You’ll work through more than 25 hands-on projects with downloadable code, schematics, PCB designs, and datasets; no machine learning background required.

You’ll build a wake-word detector, a real-time noise suppressor, an AI-powered MIDI synthesizer, a self-training battery monitor, and a person detector running a neural network on a camera board. Whether you’re an embedded developer adding AI to your products, a machine learning practitioner moving onto embedded hardware, or a maker ready to move beyond beginner projects, you’ll learn the engineering decisions behind every design. When breadboards are flaky, sensor data is noisy, or memory is tight, you’ll know how to fix it—and why.

Most projects require an Arduino UNO or Raspberry Pi Pico; a few use specialized boards. You’ll also need free software including Python with TensorFlow, Arduino IDE, and the Raspberry Pi Pico SDK. This practical approach bridges theoretical models with reliable edge deployment.