Deploying TinyML

Implement a TinyML application

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller.

Featuring faculty from:
Self-Paced
Length
6 weeks
2-4 hours per week
Certificate Price
$299
Program Dates
Start Deploying TinyML today.

What You'll Learn

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. You can preorder your Arduino kit here.

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.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will 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 your code for a microcontroller-based device
  • How to deploy your code to a microcontroller-based device
  • How to train a microcontroller-based device
  • Responsible AI Deployment

Course Overview

  • Introduction to the TinyML Kit
  • Deploying TinyML Applications on Embedded Devices
  • Collecting a Custom TinyML Dataset
  • Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
  • Profiling and Optimization of TinyML Applications

Your Instructors

Image
Vijay Janapa Reddi Headshot

Vijay Janapa Reddi

Associate Professor at John A. Paulson,
School of Engineering and Applied Sciences (SEAS),
at Harvard University
Read full bio.

Image
Pete Warden Headshot

Pete Warden

Technical Lead of TensorFlow Mobile and Embedded
at Google
Read full bio.

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.

Related Courses

Read More

Fundamentals of TinyML

Learn the language of TinyML

This online course focuses on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the language of TinyML.

Read More

Applications of TinyML

Experience TinyML in practice

This online course helps you learn first-hand how to train models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

Read More

Data Science for Business

Move beyond the spreadsheet

Designed for managers, this course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.