Tracks
Learn by outcome
Use tracks when you want a smaller path through the full curriculum: foundations, RAG, agents, inference, training, or AI lab prep.
Software engineers, students, and career switchers new to ML systems.
You can read model diagrams, run Python experiments, call LLM APIs, and explain the first full AI app path.
Builders working on search, support bots, internal knowledge assistants, and document QA.
You can design a retrieval pipeline, debug faithfulness failures, and choose vector, lexical, and graph retrieval pieces deliberately.
Engineers building coding agents, workflow agents, browser agents, or production tool-use systems.
You can design agent loops with tools, state, recovery policy, evals, and review gates.
Engineers responsible for latency, cost, local deployment, model gateways, and GPU serving reliability.
You can reason about model fit, slow responses, and which serving technique fixes each bottleneck.
Readers moving from API usage into model adaptation, post-training, and training infrastructure.
You can explain the lifecycle of a model update and choose the right adaptation method for a product constraint.
Engineers preparing for AI lab, applied research engineer, or LLM systems interviews.
You can practice the concepts interviewers probe while still learning the underlying systems deeply.