Acknowledgements
This book is inspired by the TinyML edX course and CS294r at Harvard University. It represents years of collaboration with students, researchers, and practitioners who have shaped its development. We are deeply indebted to the folks whose groundbreaking work laid its foundation.
Through this collaboration, our understanding of machine learning systems deepened, and we realized that fundamental principles apply across scales, from tiny embedded systems to large-scale deployments. This realization shaped the book’s expansion beyond TinyML to provide foundations applicable across all scales of machine learning systems implementation.
Funding Agencies and Companies
Academic Support
We are grateful for the academic support that has made it possible to hire teaching assistants to help improve instructional material and quality:
Non-Profit and Institutional Support
We gratefully acknowledge the support of the following non-profit organizations and institutions that have contributed to educational outreach efforts, provided scholarship funds to students in developing countries, and organized workshops to teach using the material:
Corporate Support
The following companies contributed hardware kits used for the labs in this book, supported the development of hands-on educational materials, provided technical tooling and debugging assistance, or provided infrastructure and hosting services:
Contributors
We express our sincere gratitude to the open-source community of learners, educators, and contributors. Each contribution, whether a chapter section or a single-word correction, has significantly enhanced the quality of this resource. We also acknowledge those who have shared insights, identified issues, and provided valuable feedback behind the scenes.
A comprehensive list of all GitHub contributors is available below, reflecting the collaborative nature of this open-source project. For those interested in contributing further, please consult our GitHub page for more information.
