Running Machine Learning on Microcontrollers — A Sample Usage of embml

Most embedded developers have heard the pitch for "TinyML" by now. Train a model in Python, quantize it, convert it, flash a frozen blob to your device. The microcontroller runs inference. It never learns. It never adapts. It just executes. That's fine for a class of problems — but it leaves a lot on the table. What if your sensor drifts after six months in the field? What if you want the device to tune itself to the specific motor it's attached to, not a generic one from a training dataset? What if there's simply no server in the loop? embml is a sample repository exploring what it looks like to do machine learning on the device itself — in pure C, with no dynamic allocation, no external dependencies beyond the standard library, and no Python runtime anywhere in the chain. 📦 Sample Repo: https://github.com/hejhdiss/embml It is not a production framework. It is a well-structured, readable starting point — a reference that embedded developers can clone, read, understand, and adapt. Ever