Kubeflow Model Registry
lastmod 2025-12-30created 2025-12-301 edits
A model registry provides a central index where machine learning model developers can index and manage models, versions, and machine learning artifact metadata. It bridges the gap between model experimentation and production activities. It also provides a central interface for all stakeholders in the machine learning lifecycle to collaborate on machine learning models.
Stages
- Create: In the create stage, the model registry facilitates collaboration among teams to track changes, experiment with different model architectures, and keep a history of model iterations.
- Verify: In the verify stage, the model registry supports rigorous testing and validation before moving on to the next stage, and keeps records of performance metrics and test results for each version.
- Package: The model registry systematically manages model artifacts and dependencies, supporting seamless integration with deployment pipelines and ensuring reproducibility across environments.
- Release: When releasing models, the model registry manages the transition of validated versions to production-ready status, helping organizations maintain versioning rules and streamline approval workflows.
- Deploy: During deployment, the model registry provides information about approved model versions and related artifacts, ensuring consistency and traceability across deployment environments.
- Monitor: In the monitor stage, the model registry keeps a comprehensive record of deployed models and links them to performance metrics, supporting continuous performance monitoring and model drift detection, and facilitating proactive maintenance and retraining as needed.