Build your own ML framework. TinyTorch is organized into four progressive tiers that take you from mathematical foundations to production-ready systems. Each tier builds on the previous one, teaching you not just how to code ML components, but how they work together as a complete system.

Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems.

Note from me, Frederick: I did not write this, but it reads exactly as if I had wrote it. Holy cow.

The Rules of Machine Learning guide provides a set of best practices and distilled wisdom from Google engineers for building, deploying, and maintaining robust and effective Machine Learning systems in production.

This prophetic Bob the Angry Flower cartoon from 2003.

(SLOGOR)

I’m Alice, a technical AI safety writer. I write the ML Safety Newsletter and my personal writing is on LessWrong. I have a background in technical ML, but pivoted to communications because I think this is where I can do the most good.

Very interesting way to avoid using Python. As a Python-hater myself, I appreciate this greatly.

Considering that ML is a pretty important field, and will continue to be going into the future, this is an option I have to keep my eye on.

This is really fascinating. Something to note is that the sample size is narrow focused on experienced developers with particular famous open source projects (average 5 years and 1,500 commits on the project in question).

In my own job I also have a novel, long-term situation so I can really sympathize with the prime slowdowns they identify. AI just doesn’t cut it.