Streamlining DSPy Development: Track, Debug, and Deploy With MLflow

DSPy is a framework for authoring GenAI applications with automatic prompt optimization, while MLflow provides powerful MLOps tooling to track, monitor, and productize machine learning workflows. In this lightning talk, we demonstrate how to integrate MLflow with DSPy to bring full observability to your DSPy development.

We’ll walk through how to track DSPy module calls, evaluations, and optimizers using MLflow’s tracing and autologging capabilities. By the end, you'll see how combining these two tools makes it easier to debug, iterate, and understand your DSPy workflows, then deploy your DSPy program — end to end.