AI Model Versioning and Management

Managing AI Model Lifecycle

Effective model versioning and management is crucial for successful AI projects. Learn best practices for tracking, versioning, and managing AI models.

Version Control Strategies

Essential practices for model versioning:

• Model Registry: Centralized model storage
• Version Tracking: Model lineage and history
• Metadata Management: Performance metrics and parameters
• Artifact Storage: Efficient model storage

Management Tools

Popular tools for model management:

• MLflow Model Registry
• DVC for model versioning
• Weights & Biases artifacts
• Custom model repositories

Best Practices

Key considerations for model management:

• Standardized naming conventions
• Automated versioning workflows
• Performance tracking
• Deployment management