A TinyML demo can be built in a weekend. A TinyML product has to survive noisy sensors, limited memory and years of updates. This is an English DEV.to draft based on a Silicon LogiX technical article. The canonical source is linked at the end. Why it matters Microcontrollers can run useful inference for anomaly detection, sound classification, gestures and low-power sensing. The difficult part is not only fitting the model. It is making the result reliable enough for the real environment. Architecture notes The pipeline starts with data collection and labeling, not with model selection. Quantization reduces memory and compute, but it can change accuracy in surprising ways. Feature extraction may consume more time and RAM than the neural network itself. A production device needs versioning for firmware, model, thresholds and calibration data. Practical checklist [ ] Collect data from real devices, not only lab captures.…