This guide walks through the process of deploying TensorFlow Lite Micro (TFLM) models on microcontrollers (MCUs), from training a model in TensorFlow to converting it to TFLM format and integrating it into an MCU project. It covers key steps such as model quantization, memory optimization, and runtime setup. The post is particularly useful for embedded developers and IoT engineers looking to bring machine learning to resource-constrained devices. With the growing demand for edge AI, this practical approach helps bridge the gap between cloud-based ML and on-device inference. The guide includes code snippets and configuration tips, making it a solid reference for production deployments.
A hands-on guide for deploying TensorFlow Lite Micro models on microcontrollers, covering training, conversion, and integration.