dspy-mlflow
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
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