Explanation:
The MLflow Model Registry is the feature designed for model versioning and managing stage transitions in MLflow. It allows users to track different versions of a model and manage its lifecycle by moving it through stages such as staging, production, and archived. This ensures that models can be easily compared, reverted if necessary, and only approved models are deployed in production environments, thereby minimizing risks.
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Which MLflow feature is essential for managing model versioning and facilitating transitions between different stages like staging, production, and archived?
A
MLflow Artifacts
B
MLflow Projects
C
MLflow Tracking
D
MLflow Model Registry