About

ABOUT

From a classroom
to five continents.

An open-source curriculum that grew into a global movement for AI engineering education.

Mission

To establish AI engineering as a foundational discipline, alongside software engineering and computer engineering, by teaching how to design, build, and evaluate end-to-end intelligent systems.

AI engineering is the discipline of building efficient, reliable, safe, and robust intelligent systems that operate in the real world, not just models in isolation.

Software engineering brought that rigor to code. Computer engineering brought it to hardware. AI engineering brings it to intelligent systems, sitting at the intersection of ML theory (understanding why models work), ML systems (understanding how to make them run), and applied ML (understanding what to build and for whom).

The world is rushing to build AI. It is not engineering it.

Students today learn algorithms divorced from the silicon that executes them, the memory hierarchies that constrain them, and the networks that distribute them. They can call model.fit() but cannot reason about why their model is slow, where the bottleneck lives, or how to fix it without guessing. The education pipeline has not kept pace with the industry it feeds.

Machine Learning Systems, the textbook and its educational ecosystem, exists to close that gap. Our thesis is simple: constraints drive architecture. You do not choose a Transformer because it is trendy; you choose it because of how it parallelizes on real silicon. We teach AI as infrastructure, governed by physical laws, shaped by engineering constraints, and measurable through quantitative reasoning.

This approach draws on a tradition that goes back decades. Hennessy and Patterson showed that computer architecture could be taught through quantitative reasoning. Andrew Ng showed that ML education could reach millions. We draw on both traditions to build something new.

But a textbook and all its associated materials only matter if they reach the people who need them. Right now, millions of engineers are building AI systems without understanding the infrastructure beneath them. We cannot fix that one classroom at a time.

So we made everything free. We open-sourced every chapter, every lab, every slide deck. We built a global network of 50+ universities who teach from the curriculum at their own institutions. We ship hardware kits to classrooms that could not otherwise afford them. And we set a goal:

1,000,000

One million learners by 2030

It started with one course at one university in 2020. Within two years, ten universities had adopted it. Today it is fifty, across five continents — and the growth has been entirely organic. No marketing, no ad spend, just word of mouth from people who found it useful:

GitHub star growth chart showing adoption over time

Everything here is free — our only ask is a ⭐ star on GitHub. It tells universities, publishers, and funders that AI engineering education matters.

How We Teach

These principles guide every chapter, lab, and lecture deck in the curriculum:

⚙️

Principles Over APIs

We teach the physics beneath the framework. Memory hierarchies, not torch.cuda(). Arithmetic intensity, not library calls. Knowledge that endures beyond any single tool.

📐

Quantitative Reasoning

Every claim is backed by a number. Students learn to estimate, measure, and verify — the same skills that separate a systems engineer from a script runner.

🌍

Open and Global

Free HTML, PDF, and EPUB. Used at universities across five continents. Licensed under CC-BY-NC-SA 4.0 so anyone can teach from it, adapt it, and build on it.

🔬

Full-Stack Curriculum

Not just a textbook. TinyTorch, hardware kits, interactive labs, lecture slides, and instructor resources — everything needed to teach a two-semester course.

22,800+ GitHub Stars

32 Chapters

35+ Lecture Decks

20 TinyTorch Modules

9,000+ Interview Questions

95+ Contributors

50+ Universities

5 Continents

Our Story

2020

A course becomes a textbook

CS 249r launches at Harvard, a graduate seminar on TinyML with no textbook. Course notes become the seed of one. In parallel, the edX TinyML Professional Certificate goes live with Google and the tinyML Foundation, reaching thousands of learners worldwide.

2021

The global network forms

A partnership with ICTP in Trieste creates the TinyML4D Academic Network. Twenty universities across Latin America, Africa, and Asia join the first cohort, each receiving curriculum, mentorship, and hardware kits. The annual SciTinyML workshop series begins.

2022

Open-source on GitHub

The textbook is published as an open repository. Educators and students from around the world begin contributing: fixing examples, proposing chapters, translating content. A second cohort doubles the TinyML4D network to 40+ universities.

2023

Community takes root

The repository passes 10,000 GitHub stars. Regional workshops expand to Morocco, Colombia, and Malawi. The monthly Show & Tell series, where students from 20+ countries demo projects on Zoom, becomes a fixture of the community calendar.

2024

Beyond TinyML

The textbook outgrows its TinyML origins. Students are asking about training at scale, distributed systems, and fleet orchestration — the full systems stack. Two volumes take shape: Foundations and At Scale. The site moves to mlsysbook.ai. IEEE formally cites the work.

2025

A full curriculum

The project grows far beyond a textbook: TinyTorch (a build-from-scratch ML framework), interactive Marimo labs, hardware deployment kits for Arduino and Seeed, lecture slides, an instructor hub, and StaffML — a physics-grounded interview question bank with thousands of verified questions spanning cloud, edge, mobile, and TinyML. MIT Press signs on for a hardcover edition.

2026

MIT Press publication

Two-volume hardcover edition forthcoming. Thousands of GitHub stars, 95+ contributors, and 50+ universities across five continents. The open-source edition remains free — always.

From a classroomto five continents.