What you'll learn

  • An understanding of the hardware of a microcontroller-based device
  • A review of the software behind a microcontroller-based device
  • How to program your own TinyML device
  • How to write code and deploy to a microcontroller-based device
  • How to train a microcontroller-based device
  • Responsible AI Deployment

Course description

Have you wanted to build a TinyML device? In Deploying TinyML, you will learn the software, write the code, and deploy the model to your own tiny microcontroller-based device. Before you know it, you’ll be implementing an entire TinyML application.

A one-of-a-kind course, Deploying TinyML is a mix of computer science and electrical engineering. Gain hands-on experience with embedded systems, machine learning training, and machine learning deployment using TensorFlow Lite for Microcontrollers, to make your own microcontroller operational for implementing applications such as voice recognition, sound detection, and gesture detection.

The course features projects based on a TinyML Program Kit that includes an Arduino board with onboard sensors and an ARM Cortex-M4 microcontroller. The kit has everything you need to build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application.

Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The third course in the TinyML Professional Certificate program, Deploying TinyML provides hands-on experience with deploying TinyML to a physical device.


  • Associate Professor at John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University
  • Technical Lead of TensorFlow Mobile and Embedded at Google

Associated Schools

  • Harvard School of Engineering and Applied Sciences

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