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Machine Learning
Model Serving
Model Versioning
ML model lifecycle management with versioning, lineage tracking, and rollback capabilities.
Intent & Description
📋 Context
Managing multiple versions of ML models in production requires systematic versioning, lineage tracking, and the ability to rollback if issues arise.
Real-world Use Case
Production ML systems where model updates need to be tracked, managed, and potentially rolled back.
Source
Advantages
- Version control for models
- Lineage tracking
- Rollback capability
- A/B testing support
Disadvantages
- Additional infrastructure
- Storage costs for multiple versions
- Management complexity
Implementation Example
# Model Versioning
model_registry.register(model, version="v2.0", lineage=metadata)