Widening Access to Applied Machine Learning with TinyML
Name
bp1r99w37uyfysnrqtthzqw7g7e9ozrt.pdf
Description
Published version
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
138a496089ae4d7551991e831233e613
Author(s)
Janapa Reddi, Vijay
Plancher, Brian
Kennedy, Susan
Moroney, Laurence
Warden, Pete
Suzuki, Lara
Agarwal, Anant
Banbury, Colby
Banzi, Massimo
Bennett, Matthew
Date Issued
January 27, 2022
Journal
Harvard Data Science Review
Publisher
MIT Press - Journals
Citation
Janapa Reddi, V., Plancher, B., Kennedy, S., Moroney, L., Warden, P., Suzuki, L., Agarwal, A., Banbury, C., Banzi, M., Bennett, M., Brown, B., Chitlangia, S., Ghosal, R., Grafman, S., Jaeger, R., Krishnan, S., Lam, M., Leiker, D., Mann, C., … Tingley, D. (2022). Widening Access to Applied Machine Learning With TinyML. Harvard Data Science Review, 4(1).
Version
Final published version
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 article, 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, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML 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 and industry 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 global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on 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 open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1162/99608f92.762d171a
