Talks: Start thinking small: Next level Machine Learning with TinyML and Python

Friday - April 21st, 2023 2:30 p.m.-3 p.m. in 255DEF

Presented by:

Experience Level:

Just starting out


We usually associate the future of computing as large clusters being able to perform tasks in a fraction of a second, but is it really the only scenario on how computational hardware will evolve?

Machine learning has become an important component in our societies, we see how people, communities, and global companies are focusing their resources into improving their technological stack, and being the leader into the next generation of AI. At the same time that we see clusters getting larger, GPUs more powerful, and our phones are practically computers being capable of doing almost everything, we do see that some of the smart devices are becoming smaller.

The Internet of Things has been flourishing for many years, and Python has been playing an important role on the “easy to automate” topic for many devices, but can Python help us in all scenarios? One of the challenges for the next generation ML is to think small, you read that right “thinking small”.

It’s time to start being able to have mechanisms with super well-trained ML models in small-devices: ML on Microcontrollers.

We are going to dive into TinyML and evaluate different setups to interact with sensors on microcontrollers. We will discuss the different hardware options and frameworks to start with, while checking different use cases that TinyML can solve, like: agriculture, conservation, health issues detection, ecology monitoring, autonomous vehicles, etc.

In this talk, you will learn about Tiny Machine Learning (TinyML), which is an approach that explores machine learning to be deployed in embedded systems that enable run ML on microcontrollers. Similarly, I will talk about Micropython and CircuitPython, and how they have been conquering the microcontroller scene. Lastly, we will discuss a real use-case, predictive machine learning model to predict anomalies for predictive maintenance problems.