Model Versioning

Overview

Model versioning is the systematic tracking of changes and improvements to AI models over time. Like version control for software, it maintains a clear history of how models evolve, enabling teams to track improvements, compare different versions, and return to previous versions when needed.

Key Components

Version tracking includes:

• Model performance metrics
• Training configurations
• Data sources used
• Important parameter changes
• Environmental settings
• Testing results

Management Benefits

Effective versioning provides:

• Clear development history
• Result reproducibility
• Change documentation
• Quality control
• Collaboration support
• Recovery options

Best Practices

Versioning should maintain:

• Systematic version numbering
• Detailed change records
• Performance comparisons
• Configuration tracking
• Access controls
• Backup procedures

Practical Applications

Versioning enables teams to:

• Track model improvements
• Compare different approaches
• Maintain quality standards
• Collaborate effectively
• Recover from issues
• Document progress

Related Topics

• Model Training
• Model Evaluation
• Model Deployment
• Model Monitoring

Related Concepts

• Model Registry
• Model Governance
• Model Deployment
• Model Evaluation
• Model Monitoring