Learn how to set up MLflow for LLM tracing and monitoring in this practical session. We’ll walk through the basics of capturing execution traces from language model applications and explore how MLflow can help you track and debug your LLM workflows. Using straightforward LangChain examples, you’ll see how to implement basic tracing functionality to gain better visibility into your model’s behavior and performance. An introduction for data scientists and ML engineers who want to add observability to their language model projects. We will also go into managing the lifecycle of experiments, runs and traces.