MLflow — Full ML Lifecycle for DSPy

Guide the user through using MLflow for DSPy auto-tracing, experiment tracking, model registry, and production deployment.

What is MLflow

MLflow is an open-source platform for the complete ML lifecycle. Its DSPy integration provides:

• Auto-tracing: mlflow.dspy.autolog() traces all DSPy calls via OpenTelemetry
• Experiment tracking: log parameters, metrics, and artifacts for optimization runs
• Model registry: version and stage optimized DSPy programs
• MLflow UI: local web UI for viewing traces, comparing experiments, and managing models

Key difference from Langtrace/Phoenix/Weave

MLflow covers the full ML lifecycle — tracing, experiment tracking, model versioning, AND deployment. The others focus primarily on observability. If you need a model registry or artifact management alongside tracing, MLflow is the right choice.

When to use MLflow