Which of the following Databricks-managed MLflow capabilities is a centralized model store?
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
The Model Registry is a centralized model store in Databricks-managed MLflow. It provides functionalities such as model versioning, lifecycle management, and model stage transitions from staging to production. This centralized repository allows for efficient management and deployment of machine learning models.
MLflow Model Registry: This is a centralized model store that helps manage the full lifecycle of MLflow Models. It provides model versioning, annotating, and stage transitions through a model's lifecycle, from staging to production. It also offers REST API for integrating with external CI/CD tools and multi-workspace capabilities for using a unified Model Registry across multiple workspaces via Unity Catalog.
The Model Registry is where trained machine learning models are stored, versioned, and managed in a centralized manner. Doc: https://docs.databricks.com/en/mlflow/model-registry.html