Deploying TinyML: Machine Learning on Microcontrollers

Bringing intelligence to everyday devices no longer requires massive cloud servers or power-hungry processors. By running machine learning directly on microcontrollers, you can build smart, responsive, and privacy-focused hardware applications. This text-only course guides you through the foundational concepts of TinyML, from understanding basic hardware constraints to deploying optimized models. You will learn how to prepare models for tiny devices, write efficient code, and successfully run edge AI applications. What you will learn: Understand the core architecture of TinyML and the constraints of microcontrollers; Prepare and convert machine learning models using TensorFlow Lite for Microcontrollers; Apply optimization techniques like post-training quantization to reduce model size; Write clean C++ code to load models and run inference on hardware; Configure low-power settings to maximize battery life in edge devices; Troubleshoot and debug common deployment issues on embedded systems. The course begins with essential terminology and the hardware fundamentals of edge computing. You will then progress step-by-step through model conversion, optimization, and deploying practical code to microcontrollers. Designed for beginners in embedded systems and machine learning, this course requires no prior hardware experience. Start reading today to bring your physical devices to life with embedded intelligence.