This article covers key techniques for deploying TinyML models on microcontrollers with limited memory (32–512 KB), including quantization strategies tailored to MCU constraints, pruning and sparse model optimization, and memory layout techniques for deterministic runtime. It also addresses how to measure tradeoffs among accuracy, latency, and power consumption, and provides a practical deployable checklist with ready-to-use scripts.