Widening Access to Applied Machine Learning with TinyML

TL;DR

This paper presents a pedagogical approach using a free online course on TinyML to democratize access to applied machine learning, emphasizing practical skills on resource-constrained devices for a global learner base.

Contribution

It introduces a comprehensive, accessible MOOC on TinyML developed through academia-industry collaboration, promoting inclusive education and practical application of ML on embedded devices.

Findings

The MOOC is openly available on edX for global learners.

Participants gain hands-on experience with TinyML applications.

The course materials are publicly released to inspire further learning.

Abstract

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.