Decoding ML (Decoding AI) · on GitHub
An end-to-end LLMOps build pairing written lessons with code: data crawling and change-data-capture, a streaming feature pipeline with advanced RAG, instruction-dataset generation, fine-tuning and evaluation, and a deployed RAG inference service with prompt monitoring. It suits ML and backend engineers who want to see every production component of an LLM system, not just the prompt.
Engineers and ML practitioners. This is an intermediate course in MLOps & AI Engineering, running about 12 lessons (10 core + 2 bonus), self-paced.
Free. MIT-licensed and free with no enrollment; running it costs roughly $1 in OpenAI API usage and under $10 on AWS, and the README promotes the paid LLM Engineer's Handbook. Price seen 2026-11-01.
Covers the full lifecycle of a deep learning product: infrastructure and tooling, troubleshooting, data management, deployment, monitoring, and ethics, with labs building a text recognizer app. Ideal for ML engineers who can train a model but need to ship one.
Andrew Ng's concise course on the ML project lifecycle: scoping, data-centric iteration, error analysis, deployment patterns, and monitoring. A fast, high-signal introduction to production thinking for anyone who has only trained models in notebooks.
Goku Mohandas's lesson series walks through designing, developing, deploying, and iterating on a production ML application with experiment tracking, testing, versioning, serving, CI/CD, and monitoring. One of the clearest end-to-end MLOps walkthroughs for engineers moving from notebooks to production.