Mlflow
MLflow ML lifecycle management. Use for ML experiment tracking.From its SKILL.md
npx -y skills add G1Joshi/Agent-Skills --skill mlflowAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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SKILL.md
1.1 KB, 285 tokens by cl100k_base, as published. Nobody here has run it
MLflow
MLflow is the standard for tracking experiments. v3.0 (2025) pivots to GenAI, adding LLM Tracing, Prompt Management, and "LLM-as-a-Judge".
When to Use
- Experiment Tracking: Logging hyperparameters (
lr=0.01) and metrics (accuracy=0.98). - GenAI Tracing: Visualizing the full chain of a RAG application.
- Model Registry: Versioning models (
my-model/v3) for deployment.
Core Concepts
Tracking URI
Where logs are stored (local ./mlruns or remote http://mlflow-server).
Autologging
mlflow.autolog() automatically captures params from Scikit-learn, PyTorch, etc.
LLM Tracing
OpenTelemetry-based tracing to debug prompt chains.
Best Practices (2025)
Do:
- Use
mlflow.evaluate(): To run "LLM-as-a-Judge" metrics on your RAG pipeline. - Use Prompt Engineering UI: MLflow 3.0 has a UI to iterate on prompts.
Don't:
- Don't use it for data storage: Log artifacts (models), not datasets. Log metadata about datasets instead.
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.