Work through these banks so you can answer theory screens with precision, connect every topic to how systems are trained, deployed, and triaged, and show staff-level judgment on tradeoffs—not just definitions. The core track builds depth from classical ML through Transformers, LLM science, RL, and MLOps; the applied track focuses on shipping LLM products: prompting, RAG, agents, evaluation, and operations. A separate design module (coming next) builds long-form architecture walkthroughs on top of this vocabulary.
Gain end-to-end ML maturity: statistics and classical methods, deep learning and NLP, attention and Transformer internals, LLM training and alignment, reinforcement learning basics, MLOps, and responsible AI—so you can justify modeling and infra choices in depth interviews.
Build fluency for LLM product and platform roles: prompt strategy, retrieval and embeddings, APIs and serving, agents and orchestration, evaluation and telemetry, LLMOps, fine-tuning, multimodal interfaces, and safety—so you sound like someone who has shipped and operated production copilots.