Architecting TinyML for the Edge

Deploying machine learning models to edge devices requires a fundamentally different approach to engineering. You're working within strict constraints — kilobytes of RAM, limited compute, no internet connectivity, and aggressive power budgets.

In my TinyML posture detection system, I optimized a model using post-training quantization (Int8), reducing its size from 120KB to 15KB while maintaining accuracy. This enabled real-time inference on an Arduino Nano 33 BLE Sense, consuming less than 1mW.

The system is built as a complete pipeline: IMU data collection → preprocessing & windowing → feature extraction → model training → TensorFlow Lite conversion → quantization → embedded deployment.

The result is a fully on-device, privacy-preserving ML system — no cloud dependency, no latency, and consistent real-time performance.