Why MLflow?
MLflow is my go-to platform for managing the complete ML lifecycle - from experimentation to production deployment. It provides essential tracking, reproducibility, and deployment capabilities without vendor lock-in.
Core Components
MLflow Tracking
- Log parameters, metrics, and artifacts
- Compare experiments and runs
- Organize with tags and notes
- Query API for programmatic access
MLflow Projects
- Reproducible ML code packaging
- Conda/Docker environment specification
- Git integration for versioning
- Remote execution capabilities
MLflow Models
- Standard model packaging format
- Multiple framework support
- Model serving with REST API
- Model registry for lifecycle management
MLflow Registry
- Centralized model store
- Version management
- Stage transitions (staging → production)
- Annotations and descriptions
My Experience at CML Insights
I've implemented MLflow as the core of our MLOps infrastructure:
Experiment Tracking
- Automated logging from training scripts
- Custom metrics for business KPIs
- Artifact storage (models, plots, data samples)
- Hyperparameter tracking
Model Registry
- Centralized model catalog
- Approval workflows for production
- Model lineage and dependencies
- Performance monitoring
Deployment Pipeline
- Models packaged in MLflow format
- Automated testing before promotion
- Kubernetes deployment integration
- Rollback capabilities
Integration with Other Tools
- Kubeflow for orchestration
- Kubernetes for model serving
- PostgreSQL for backend store
- S3 for artifact storage
- Grafana for metrics visualization
Best Practices
- Consistent logging across all projects
- Semantic versioning for models
- Comprehensive artifact storage
- Automated model validation before promotion
- Clear model documentation and metadata