MLflow
What it is: Open source platform for managing the ML lifecycle including experimentation, reproducibility, deployment, and model registry.
What It Does Best
Experiment tracking simplified. Log parameters, metrics, and artifacts with one line of code. Track thousands of experiments and compare them easily in a visual UI.
Framework-agnostic. Works with any ML library: scikit-learn, PyTorch, TensorFlow, XGBoost. Unified tracking API regardless of framework.
Model registry. Version, stage, and deploy models with lineage tracking. Know exactly which data and code produced each model.
Key Features
Tracking: Log parameters, metrics, models, and artifacts
Projects: Package code in reproducible format
Models: Deploy to various platforms (Docker, cloud, etc.)
Registry: Manage model lifecycle and versions
UI: Compare runs and visualize metrics
Pricing
Free: Open source (Apache 2.0 license)
Databricks: Managed MLflow in Databricks platform
Self-hosted: Free to run on your infrastructure
When to Use It
✅ Need to track ML experiments systematically
✅ Want framework-agnostic tracking
✅ Managing model versions and deployments
✅ Team needs to collaborate on experiments
✅ Building reproducible ML workflows
When NOT to Use It
❌ Need full MLOps platform (ClearML more complete)
❌ Want cutting-edge experiment tracking UI (W&B better)
❌ Working solo on tiny projects (overkill)
❌ Need pipeline orchestration primarily (Airflow better)
❌ Require extensive built-in visualizations
Common Use Cases
Hyperparameter tuning: Track hundreds of experiment runs
Model comparison: Compare different algorithms and features
Model registry: Manage staging and production models
Team collaboration: Share experiments and reproduce results
Model deployment: Deploy to cloud or local serving
MLflow vs Alternatives
vs Weights & Biases: MLflow self-hostable, W&B better UI/UX
vs ClearML: MLflow simpler, ClearML more features
vs TensorBoard: MLflow framework-agnostic, TensorBoard TensorFlow-focused
Unique Strengths
Industry standard: Most widely adopted ML tracking tool
Databricks backing: Well-maintained with strong support
Framework-agnostic: Works with any ML library
Simple API: Easy to add to existing code
Bottom line: De facto standard for ML experiment tracking. Best choice for teams that need simple, self-hostable experiment tracking without vendor lock-in. Not as feature-rich as commercial tools but widely adopted and battle-tested.