Machine Learning Systems
TWO-VOLUME TEXTBOOK
Machine Learning
Systems.
Two volumes. One curriculum.
The physics of AI engineering.
A rigorous, principles-first treatment of how ML systems
are built, optimized, and deployed—from a single
machine to fleet-scale infrastructure.
TINYTORCH
Build it.
From scratch.
20 interactive modules.
Zero magic.
Understand the inner workings of modern ML frameworks by building your own tensor library, automatic differentiation engine, and neural network modules in Python.
A pedagogical framework for learning ML systems engineering.
MLSYS·IM
Model the
trade-offs.
One command.
Every bottleneck.
A first-principles modeling engine for reasoning about ML system performance. Evaluate training, serving, and distributed configurations before committing hardware or code.
Configure. Model. See every bottleneck before committing hardware.
INTERACTIVE LABS
Learn by
doing.
Jupyter and Marimo.
Coming Summer 2026.
A complete suite of interactive notebooks designed to accompany the textbook. Profile performance, optimize kernels, and explore distributed training configurations.
Predict, explore, and break ML systems through interactive notebooks.
HARDWARE KITS
Deploy to
the edge.
Real silicon.
Real constraints.
Take your models out of the cloud and into the physical world. Hands-on deployment labs using Arduino, Raspberry Pi, and Seeed Studio hardware.
Microcontrollers, single-board computers, and specialized accelerators.