AI + Hardware

put a model on real hardware

This track teaches you how to move an AI model from a cloud service onto physical hardware, and it is for anyone who wants to run AI directly on robots, edge devices, or other constrained systems. After completing the chapters you will be able to take an existing model and embed it in firmware, deploy it on a robot, and operate it on low‑power hardware while handling latency, power consumption, and failure modes. You will learn the steps needed to integrate AI assistants into embedded code, adapt models for limited resources, and test them on real devices. Start with the first chapter, "AI for robotics & edge devices," which introduces the concepts and tools you need. Follow the material in the order presented, as each section builds on the previous one. If you are pressed for time, you can skim the background discussion of network limitations and go straight to the practical deployment steps, but skipping those explanations may leave gaps in understanding how latency and power constraints affect your design.