TinyML Fundamentals: Deploying Models to Microcontrollers

Bringing machine learning to edge devices opens up a world of smart, low-power applications. As embedded systems grow more capable, the ability to run AI locally without cloud dependency is an essential skill for modern developers. This course guides you through the process of taking machine learning models and fitting them onto tiny, resource-constrained microcontrollers. Through clear written explanations and code snippets, you will learn how to adapt standard models, apply modern optimization techniques, and execute them on embedded hardware using TensorFlow Lite. What you'll learn: • Understand the core concepts of TinyML, edge computing, and embedded hardware constraints. • Prepare and convert machine learning models for edge deployment using TensorFlow Lite. • Apply modern model quantization techniques to reduce memory footprint and improve power efficiency. • Write C++ code to load, configure, and execute models within microcontroller environments. • Process basic sensor data locally for real-time inference without internet connectivity. • Explore foundational edge MLOps concepts for managing embedded machine learning lifecycles. You will start with key terminology and the foundational definitions of edge AI before moving into practical, text-based coding exercises that walk you through the model conversion and deployment pipeline step-by-step. Designed for beginners with basic programming knowledge who want to explore embedded machine learning, this course requires no prior experience with hardware deployment. Start reading today to build the foundation for your first intelligent microcontroller application.