[Open Call] Participate in editing Machine Learning Systems with TinyML

[MatthewJeffson]

Open Call in editing Machine Learning Systems

We are excited to announce our official participation in the collaborative effort to edit and enhance the book Machine Learning Systems with TinyML. This project is spearheaded by Prof. Vijay Janapa Reddi of Harvard University, who serves as the primary author, editor, and curator.

We’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind Machine Learning Systems with TinyML. We go beyond the traditional textbook model to create a living knowledge hub.

Responsibilities:

Our team will focus on:

• Streamlining laboratory procedures(labs). - Vendor-neutral lab template
• Ensuring uniformity across all presented hardware installations.
• Updating and maintaining technical accuracy with the oversight of Prof. Reddi.

For any discussions related to this project, please feel free to leave your comments here. Our application engineers are on hand to assist you.

Launch Details:

The main version of the book will be made available on mlsysbook.ai. We encourage all team members to visit the site, familiarize themselves with the content, and prepare for our active involvement.

How You Can Help:

We are calling on our contributors who have expertise in Machine Learning, TinyML, and systems integration to join this initiative. Your knowledge and experience will be invaluable in refining the content and practical demonstrations outlined in the book.

What do we offer?

We want to express our gratitude to our contributors by offering a range of rewards. Depending on the difficulties(Tier 0/1/2/3), the time we expect(Days 1/3/7/15), the actual submission time and the actual submission contents, we will offer different levels of rewards.

1. We will provide our products or cash bonus to our contributors as a token of appreciation.
2. For those contributors who join our "Ranger Program", we offer even more exciting rewards, including access to our prototype products.
3. In addition, all contributors and their contributions will be acknowledged and showcased on our wiki platform.

You will be working with

Vijay Janapa Reddi	Ikechukwu Uchendu	naeemkh	Douwe den Blanken	Shanzeh Batool
Kleinbard	Jared Ping	Matthew Stewart	eliasab16	ishapira
Marcelo Rovai	Maximilian Lam	Jayson Lin	Jeffrey Ma	Sophia Cho
Korneel Van den Berghe	Zishen	Colby Banbury	Srivatsan Krishnan	Andrea Murillo
Alex Rodriguez	Aghyad Deeb	Divya	Aghyad Deeb	arnaumarin
Abdulrahman Mahmoud	Michael Schnebly	ELSuitorHarvard	oishib	Jared Ni
Emil Njor	Mark Mazumder	Sara Khosravi	Henry Bae	Jae-Won Chung
YU SHUN, HSIAO	Aditi Raju	Pong Trairatvorakul	eurashin	Jennifer Zhou
Andrew Bass	Marco Zennaro	Shvetank Prakash	Alex Oesterling	Bruno Scaglione
Gauri Jain	Eric D	Allen-Kuang	Sercan Aygün	Annie Laurie Cook
Curren Iyer	yanjingl	Yang Zhou	abigailswallow	Batur Arslan
Costin-Andrei Oncescu	Yu-Shun Hsiao	songhan	Jessica Quaye	Emeka Ezike
happyappledog	Jason Yik	Shreya Johri	Sonia Murthy	Vijay Edupuganti
The Random DIY

[MatthewJeffson]

We have under contents/labs/seeed/xiao_esp32s3/, five main folders, one for each lab:

• Setup
• Image Classification
• Object Detection
• KWS
• Motion Classification and Anomaly Detection

Each folder contain the .qmd and .bib files and its images (split in. jpeg and .png). We push a revised versions of my ESP32S3 tutorials.

Another goal is to “connect" parts of such laboratories with book chapters.

• For example, on ML Systems, we can link the Labs at the end to the “Setup lab”:

• Or Data Engineering Labs with the Dataset acquisitions across all specific labs (vision, audio and IMU):

[github-actions]

✅ @MatthewJeffson

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