TinyML: Deploying Machine Learning Models on Edge and IoT Devices

How I Brought AI to Microcontrollers with TensorFlow Lite and Edge Impulse

When I first started experimenting with TinyML, I wasn’t sure if machine learning could even fit on something as small as an Arduino or ESP32.
But after months of testing, optimizing, and rewriting models, I realized — TinyML isn’t just possible; it’s the future of AI deployment.

In this article, I’ll walk you through my personal experience building and deploying TinyML models — from training on a laptop to running inference on edge devices consuming less than 50 KB of RAM.

Let’s dive deep into how machine learning shrinks down for IoT hardware — with rich explanations and code examples.

1. Understanding TinyML: Machine Learning on the Edge

TinyML means deploying machine learning models on low-power microcontrollers.
Unlike cloud AI, edge AI models:

• Run offline (no internet needed)
• Use low memory (as small as 16 KB RAM)
• Require ultra-low power (< 1 mW)

Typical TinyML Hardware:

• Arduino Nano 33 BLE…