Difference between Workspace and Unity Catalog experiments when using MLflow autologging?

[kunduruanil]

Hi everyone,

I am trying to understand the exact differences between using Workspace experiments versus Unity Catalog experiments, specifically in the context of MLflow autologging (mlflow.autolog()).

Does autologging behave differently depending on whether the experiment is registered in the Workspace or in Unity Catalog (using a 3-level namespace)? Are there any limitations, best practices, or specific configurations I should be aware of when using autologging with Unity Catalog compared to the traditional Workspace setup?

Any insights or documentation links would be greatly appreciated. Thanks!

[gowri_databrick]

@kunduruanil  Hi,

As far as I understand, mlflow.autolog() works in basically the same way with Workspace and Unity Catalog experiments. It automatically logs supported parameters, metrics, models, and other information from the ML training run.

The main difference is how the experiments and MLflow assets are managed and governed. Workspace experiments are tied to the workspace, while Unity Catalog gives you more centralized governance and access control.

For production use, Unity Catalog can be a better option when multiple teams need controlled access and governance.

I would also check the Databricks Runtime and MLflow versions, since some features and configurations may depend on the version you are using.

Hope this helps!

[kunduruanil]

@gowri_databrick  Mlflow version is 3.8.1 and I want to control how and where my experiments are stored!! Do you know how to do that?