The Rise of TinyML: Machine Learning on a Microcontroller

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In the past, machine learning was the domain of cloud servers, GPUs, and heavy-duty hardware. Today, that’s changing — dramatically. With TinyML, developers can now deploy ML models on microcontrollers as small and affordable as an ESP32, STM32, or even an Arduino Nano 33 BLE Sense. These are chips that cost less than a cup of coffee and can run for months on a coin cell battery.

🧠 Tiny Models, Real Intelligence

Despite their size, these microcontrollers can now handle tasks like:

  • Keyword spotting (“Hey IoT!” to wake up a device)
  • Gesture recognition using accelerometers
  • Anomaly detection in machines or wearables
  • Visual classification with ultra-low-res cameras

All of this happens on the edge — locally, without needing a cloud connection. 

That’s critical for applications in remote areas, latency-sensitive systems, and privacy-first devices where you can’t (or shouldn’t) send data off-site.

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