llm-twin-course

Why we included this project

If you are an ML or backend engineer tired of notebook demos that fall apart outside a demo, this free, self-paced course takes you from raw data to a deployed API for an LLM-powered writing assistant. You follow a concrete architecture with data collection, feature, training, and inference pipelines, touching crawlers, a queue, a vector store, fine-tuning, and RAG. The real value is in the operational glue most tutorials skip: experiment tracking, model registries, prompt monitoring, versioning, and serverless deployment on AWS, all shown with working examples. The 12 lessons pair written theory with runnable source code, and bonus lessons show how to refactor the RAG layer. It is a solid reference for seeing how LLMOps practices fit together in one codebase before designing your own system.