# Mlflow

> Manages the machine learning lifecycle with experiment tracking, model versioning, reproducible runs, and deployment through the MLflow platform.

- Skill: `neuralblitz/mlflow` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/mlflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/mlflow/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Model Training & Fine-tuning
- Tags: Experiment Tracking, Ml Lifecycle, Mlflow, Model Deployment, Model Registry, Model Versioning
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/neuralblitz/mlflow

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# MLflow

MLflow is an open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. It provides tracking, projects, and model registry.

## Key Concepts

- Tracking server
- Projects packaging
- Model registry
- Model serving
- Artifact storage

## Common Use Cases

- Experiment tracking
- Model versioning
- Reproducible runs
- Model deployment
- Collaboration

## Best Practices

- Use consistent experiment naming
- Log all parameters and metrics
- Version datasets with artifacts
- Use model registry for promotion
- Implement proper access control

## Resources

- Docs: mlflow.org/docs
- Related Skills: mlops, weights-biases, mllmops