New TinyML Professional Certificate Program from HarvardX and Google’s Open-Source Machine Learning Platform, TensorFlow

Today, we are excited to announce a brand new, first-of-its-kind TinyML Professional Certificate program created by HarvardX and Google’s Open-Source Machine Learning Platform, TensorFlow. TinyML (Tiny Machine Learning) is the latest embedded software technology shaping design and innovation for products that offer always-on monitoring or feedback. Think about the potential for invention – from wildlife tracking to smart devices, the possibilities for this new space are endless. 

We teamed up to deliver a Professional Certificate program that can help you get up to speed on all things TinyML, including the opportunity to work on a TinyML device with an at-home kit. Professional Certificates on edX are a series of courses designed by industry leaders and top universities to build and enhance critical professional skills needed to succeed in today’s most in-demand fields.

Read more via snippets from TensorFlow’s blog post below (originally posted here) and sign up here to receive updates regarding the program’s launch, planned for early Fall 2020.  


FromTensorFlow…The Future of ML Is Tiny and Bright

About TinyML

TinyML is one of the fastest-growing areas of Deep Learning. In a nutshell, it’s an emerging field of study that explores the types of models you can run on small, low-power devices like microcontrollers

TinyML sits at the intersection of embedded-ML applications, algorithms, hardware and software. The goal is to enable low-latency inference at edge devices on devices that typically consume only a few milliwatts of battery power. By comparison, a desktop CPU would consume about 100 watts (thousands of times more!). Such extremely reduced power draw enables TinyML devices to operate unplugged on batteries and endure for weeks, months and possibly even years — all while running always-on ML applications at the edge/endpoint.

Although most of us are new to TinyML, it may surprise you to learn that TinyML has served in production ML systems for years. You may have already experienced the benefits of TinyML when you say “OK Google” to wake up an Android device. That’s powered by an always-on, low-power keyword spotter, not dissimilar in principle from the one you can learn to build here

The difference now is that TinyML is becoming rapidly more accessible, thanks in part to TensorFlow Lite Micro and educational resources like this upcoming HarvardX course.

TinyML unlocks many applications for embedded ML developers, especially when combined with sensors like accelerometers, microphones, and cameras. It is already proving useful in areas such as wildlife tracking for conservation and detecting crop diseases for agricultural needs, as well as predicting wildfires.

TinyML can also be fun! You can develop smart game controllers such as controlling a T-Rex dinosaur using a neural-network-based motion controller or enable a variety of other games. Using the same ML principles and technical chops, you could then imagine collecting accelerator data in a car to detect various scenarios (such as a wobbly tire) and alert the driver.

Fun and games aside, as with any ML application— and especially when you are working with sensor data—it’s essential to familiarize yourself with Responsible AI. TinyML can support a variety of private ML applications because inference can take place entirely at the edge (data never needs to leave the device). In fact, many tiny devices have no internet connection at all. 

More about the short courses

The HarvardX course is designed to be widely accessible to everyone. You will learn what TinyML is, how it can serve in the world, and the possibilities to unlock its bright future. 

The course starts with the basics, including how to collect data, how to train basic tiny ML models, and how to deploy them using TensorFlow Lite for Microcontrollers

In one workflow, you’ll build a TensorFlow model using Python in Colab (as always), then convert it to run in C on a microcontroller. The course will show how to optimize the ML models for severely resource-constrained devices (e.g., those with less than 100 KB of storage). And it includes various case studies that examine the challenges of deploying TinyML “into the wild.”

Take TinyML Home

We’re excited to work closely with Arduino and HarvardX to make this experience possible. 

An off-the-shelf TinyML kit from Arduino will be available to edX learners for purchase. It includes an Arm Cortex-M4 microcontroller with onboard sensors, a camera and a breadboard with wires—everything needed to unlock the initial suite of TinyML application capabilities, such as image, sound and gesture detection. Students will have the opportunity to invent the future.

We’ll feature the best student projects from the course right here on the TensorFlow blog

We’re excited to see what you’ll create!