Model Versioning
Systems for tracking, managing, and deploying different versions of AI models throughout development and production lifecycles.
Definition
Model versioning provides systematic tracking of AI model changes, performance metrics, and deployment history throughout development and production cycles. This includes managing model artifacts, metadata, and associated code versions.
Versioning systems enable rollbacks to previous models, A/B testing between versions, and audit trails for compliance and debugging purposes while maintaining reproducibility of model training and deployment processes.
Why It Matters
Proper model versioning prevents deployment disasters, enables rapid rollbacks when issues arise, and provides accountability for model performance changes. This is essential for maintaining reliable AI systems in production.
Businesses need versioning to manage risk, comply with regulations, and maintain service quality as AI systems evolve and improve over time without disrupting operations.
Examples in Practice
Spotify maintains multiple recommendation model versions simultaneously, gradually rolling out improvements while monitoring performance and user satisfaction metrics.
Financial institutions track fraud detection model versions to comply with regulatory audit requirements and quickly revert to previous versions if new models show unexpected behavior.
Autonomous vehicle companies version control driving models with detailed performance tracking to ensure safety improvements and enable rapid deployment rollbacks if issues emerge.