Machine Learning Systems with TinyML: Join the Open-Source Movement to Accelerate ML Development at the Edge

We are excited to introduce “Machine Learning Systems with TinyML” an open-source textbook written, edited and curated by Prof. Vijay Janapa Reddi from Harvard University. This comprehensive academic textbook explores building and deploying ML systems, emphasizing TinyML to illustrate end-to-end concepts. It covers data engineering, model optimization, edge deployment, and sustainable AI, aiming to bridge the gap between theory and practical application in ML systems.

A Collaborative Effort

The creation of this book is a collaborative effort, driven by a shared vision of creating a reference material that encompasses the vast expanse of knowledge and insights gained throughout the study of Machine Learning Systems. It is a work in progress, serving as a valuable resource for anyone venturing into the world of Machine Learning Systems. You can see the full list of individual contributors here.

We are proud to share that Seeed Studio’s application engineers have also contributed to the content, enriching the book with practical insights and real-world applications.

Call for Contributors

This book welcomes contributions from everyone. Your contributions are welcome and can encompass a variety of tasks, such as:

  • Identifying and reporting any bugs in the examples
  • Correcting typographical errors in the documentation
  • Contributing additional examples
  • Authoring a new chapter
  • Suggesting topics for new chapters
  • Enhancing the accessibility of the material

Hardware Donation

In addition to content co-creation, Seeed Studio is donating a tinyML kit (XIAO ESP32S3 Sense and Micro SD Card) for every 25 stars received on the book’s GitHub repository, up to 1000 stars. These kits will act as the course materials for students across institutions in developing countries through the tinyML4D program, supported by Harvard University, the tinyML Foundation, and the Abdus Salam International Centre for Theoretical Physics (ICTP). These compact devices will enable recipients to explore the vast possibilities of TinyML and develop innovative solutions to local challenges.

(Seeed Studio XIAO ESP32S3 Sense)

The TinyML4D program is dedicated to enabling innovative solutions for the unique challenges faced by developing countries, improving global access to educational materials for the cutting-edge field of TinyML. Your support can make a difference! ⭐️ Star the GitHub repository now to support this educational initiative and be part of a global movement advancing machine learning education.

While we initially start from 40 kits, discussions are ongoing to establish a more resilient mechanism for continuous support, hoping to bring the benefits of TinyML to regions where it can make the most significant impact. Stay tuned for more updates!

More About the Book

“Machine Learning Systems with TinyML” offers a comprehensive look at ML systems development, including:

  • Data Collection: Methods for gathering and processing data.
  • Model Design: Techniques for designing efficient ML models.
  • Optimization and Acceleration: Strategies for optimizing ML models for performance.
  • Security Hardening: Ensuring robust and secure ML deployments.
  • Integration: Seamlessly integrating ML systems into real-world applications.

The book, structured through the lens of TinyML, makes complex concepts accessible and practical for in-class learning. It also addresses ethical considerations and societal challenges in deploying AI. Readers will:

  • Understand Core Concepts: By covering data collection, model design, optimization, security, and integration, the book ensures a well-rounded understanding of ML systems.
  • Apply Theory to Practice: Practical insights and real-world applications make the transition from theoretical knowledge to practical implementation seamless.
  • Stay Updated: The book’s open-source nature means it is continually updated to reflect the latest advancements in the field.

Whether you’re just starting or looking to deepen your understanding, this book offers valuable knowledge that can propel your journey in machine learning.

Join the Movement

“Machine Learning Systems with TinyML” is more than just a textbook; it’s a collaborative, ever-evolving resource aimed at bridging the gap between theoretical and practical aspects of machine learning systems. Dive into “Machine Learning Systems with TinyML” and become an integral part of a global effort to make AI more accessible, efficient, and impactful. Let’s build the future of machine learning together!

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June 2024