Courses

for AI Engineers

Training models, not just calling them, and then keeping them correct after they ship. The roadmap calls this "the infrastructure-and-training track, distinct from AI engineering": it "demands more mathematics and more systems depth", and it is for people who "want to build the systems that produce and serve models". Its central claim is the one this course is built around: "Training a model is a bounded problem with a clear finish. Keeping a model correct in production is unbounded, and that is

Architecting a whole AI system rather than building its parts: what to put where, what to leave out, and which tradeoffs you are choosing deliberately. This is the layer above every other course in the series, and it doubles as preparation for AI system design interviews, which reward the same thinking that produces good architecture.

Learn to program in Python from the very first line, up to writing a small, tested package with classes, files and a command-line tool. No experience needed.

How computers read text, from characters and regular expressions to word vectors, transformers and large language models, measured on one real-shaped inbox from the first lesson to the last.

How AI applications connect to tools and data through one open protocol: why it exists, what goes over the wire, how to build servers and hosts, how to authorise and secure them, and how to run them in production, built on one small project from the first lesson to the last.

Most explanations of transformers stop at a diagram of attention. This course goes all the way through: you derive the mechanics, build the model, train it, scale it, serve it cheaply, adapt it, and then take it apart to see what it learnt.

How to make a language model answer from your documents, correctly, with citations, and say so when it cannot.

What a large language model is, how it reads and writes, how to make it reliable, how to test it, how to secure it and how to run it, measured on one real-shaped task from the first lesson to the last.

Engineering done inside someone else's organisation, on their data, against their constraints, next to the people whose problem it is.

Learn to build AI applications that keep working when real people use them.

Systems that decide what to do next, act on the world, and keep going without you watching. Everything worth learning to build them, and to keep them from causing damage.