Practical MLflow for Generative AI on Databricks

Chapter 10. Unifying GenAI Systems with MLflow

Throughout this book, we have explored the main pieces of developing, deploying, and managing GenAI applications with MLflow. From prompt engineering and versioning to tracing, evaluation, deployment, and continuous improvement, you now have a foundation for building end-to-end GenAI systems.

By the time a GenAI application reaches production, calling the model is usually the easy part. The harder problem is getting many moving pieces to operate coherently. Your application framework, agent runtime, retrieval layer, tools, background services, IDE assistant, and evaluation stack may all produce useful signals, but if each one speaks a different dialect, every failure becomes harder to investigate and every improvement becomes harder to verify.

This is where MLflow’s role expands, which is the subject of this chapter. Beyond serving as a tracing library, prompt tool, or evaluation API in isolation, MLflow also functions as an integration plane for GenAI systems: one that provides a common artifact in the trace, a serving surface for runtime access, and consistent workflows for evaluation and improvement.

What that looks like in practice: requests can be observed in one place, assistants can investigate the same evidence programmatically, coding agents can follow repeatable repair workflows, and third-party judges can plug into a common evaluation loop. Figure 10-1 shows this idea at a high level, connecting runtime execution, serving, ...