Practical MLflow for Generative AI on Databricks

Chapter 2. End-to-End GenAI Application Lifecycle with MLflow

If Chapter 1 was our friendly neighborhood tour of MLflow, this chapter is the city transit map. We will zoom out just enough to see how a modern GenAI application is actually developed, from the first prototype that answers coherently to a production system that improves on purpose. GenAI work is a loop that gathers momentum only when you can see what happens, measure what matters, and change the right thing next.

MLflow 3.x is built for that loop. MLflow Tracing turns every execution into a replayable record, preserving inputs, outputs, intermediate steps, timing, and metadata. The evaluation harness transforms “looks good” into evidence by applying consistent judgments to realistic scenarios. Versioning and lineage make it possible to say what is running, why it deserves to be there, and how to roll back cleanly. Together, these capabilities provide the memory and structure required to evolve a GenAI application deliberately rather than by relying on instinct.

In this chapter, we introduce a five-phase lifecycle that frames GenAI work as a disciplined system. Each phase has a distinct purpose:

Develop

Focuses on building a traceable, testable application

Evaluate

Formalizes quality before deployment, using curated datasets and consistent scorers

Deploy

Promotes versioned artifacts through controlled release surfaces

Monitor

Observes real-world behavior through structured traces and measurable signals ...