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Introduction
MLflow Components
MLflow Tracking
- Logs experiments, parameters, metrics, and artifacts
- Provides UI for comparing runs and visualizing results
- Supports automatic logging for popular ML libraries
Use case: Track model performance across different hyperparameters, compare experiment results
MLflow Projects
- Packages ML code in reusable, reproducible format
- Uses conda.yaml or requirements.txt for dependencies
- Supports different execution environments (local, cloud, Kubernetes)
Use case: Share reproducible ML workflows, standardize project structure
MLflow Models
- Standardizes model packaging and deployment
- Supports multiple ML frameworks (scikit-learn, TensorFlow, PyTorch, etc.)
- Enables model serving via REST API, batch inference, or cloud platforms
Use case: Deploy models consistently across environments, A/B test different model versions
MLflow Model Registry
- Centralized model store with versioning and stage management
- Tracks model lineage and metadata
- Supports approval workflows and access controls
Use case: Manage model lifecycle from staging to production, collaborate on model deployment
Common Use Cases
Experiment Management
- Compare model architectures, hyperparameters, and feature engineering approaches
- Track training metrics over time and across team members
Model Deployment
- Package models for consistent deployment across dev/staging/prod
- Serve models as REST endpoints or batch processing jobs
Collaboration
- Share reproducible experiments and models across data science teams
- Maintain audit trail of model development and deployment decisions
MLOps Workflows
- Automate model training, validation, and deployment pipelines
- Integrate with CI/CD systems for continuous model delivery
MLflow works well as a lightweight, open-source solution that integrates with existing ML workflows without requiring major infrastructure changes.