ML Model Management
ML Model Management is the process of organizing, tracking, maintaining, and governing machine learning models throughout their lifecycle, from development and testing to deployment, monitoring, updating, and retirement.
What is ML Model Management?
ML Model Management includes practices for tracking model versions, configurations, training data, performance metrics, deployments, and changes over time. It helps teams maintain a clear record of models and their associated artifacts while managing which versions are tested, approved, deployed, or retired.
Why is ML Model Management Important?
Effective model management improves reproducibility, governance, and operational control across the machine learning lifecycle. It helps teams quickly identify which model is running in production, roll back problematic versions, monitor model performance, and manage updates consistently.
Common use cases
ML Model Management is commonly used for model versioning, deployment tracking, model monitoring, performance management, compliance, auditing, and maintaining multiple models across production environments.