Tracking model versions using MLflow Model Registry

While the MLflow Tracking server lets you track all the attributes of your ML experiments, MLflow Model Registry provides a central model repository that lets you track all the aspects of your model life cycle. MLflow Model Registry consists of a user interface and APIs to track the model's version, lineage, stage transitions, annotations, and any developer comments. MLflow Model Registry also contains webhooks for CI/CD integrations and a model server for online model serving.

MLflow Model Registry provides us with a way to track and organize the many ML models that are produced and used by businesses during development, testing, and production. Model Registry provides a secure way to share models by leveraging access control lists and provides a way to integrate with model governance and approval workflows. Model Registry also allows us to monitor ML deployments and their performance via its API.

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