LLMOps: Fundamentals of LLMOps

This beginner-level, self-paced course introduces LLMOps, the practice of building, running, evaluating, and improving applications powered by large language models. Learners set up an LLMOps toolchain step by step, build a functional LLM app with prompt templating and a Streamlit UI, and implement a basic Retrieval-Augmented Generation (RAG) pipeline using LangChain and vector databases. The hands-on curriculum also covers evaluating, debugging, and observing LLM systems with Langfuse, and helps learners map LLMOps roles to their own professional background.

• Who This Course is For
  • Beginners interested in building and operating LLM-powered applications
  • Learners comfortable with Python and basic programming
  • No prior LLMOps or machine-learning operations experience required
• What to Expect
  • Self-paced, hands-on course with step-by-step environment and tool setup
  • Work with notebooks, LLM APIs, prompt templating, and Streamlit
  • Use LangChain, vector databases, and Langfuse for RAG, tracing, and evaluation
• What You'll Achieve
  • Build basic LLM apps and a simple RAG pipeline for document Q&A
  • Evaluate LLM outputs with structured criteria and debug application behavior
  • Map LLMOps roles to your background and plan next career steps