Answer-first summary for fast verification
Answer: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.
Option B is the most cost-effective and secure solution because it leverages Unity Catalog's built-in capabilities for cross-workspace model sharing. By logging models directly into Unity Catalog and configuring appropriate READ permissions for the dev workspace, the engineer avoids the costs and complexity of duplicate training pipelines (C), manual export/import scripts (D), or external registries (A). Unity Catalog provides centralized governance, audit trails, and fine-grained access control, ensuring security while minimizing operational overhead. The community discussion shows 100% consensus on B, and while one comment mentions D based on documentation, B aligns with Databricks best practices for secure, cost-effective model sharing across Unity Catalog-enabled workspaces.
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A Generative AI Engineer has fine-tuned LLMs in their production Databricks workspace and wants to make them available for testing in their development workspace. All workspaces are enabled for Unity Catalog, and models are currently being logged to the MLflow Model Registry.
What is the most cost-effective and secure method for the engineer to achieve this?
A
Use an external model registry which can be accessed from all workspaces.
B
Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.
C
Setup a duplicate training pipeline in dev, so that an identical model is available in dev.
D
Setup a script to export the model from prod and import it to dev.