Can add support to Unity Catalog registered models?

[dangz90]

Willingness to contribute

Yes. I would be willing to contribute this feature with guidance from the MLflow community.

Proposal Summary

The use case for this feature is to add support to MLflow Recipes to save models directly to Databrick's Unity Catalog.

Motivation

What is the use case for this feature?

The use case for this feature is to enable MLflow Recipes to save models directly to Databrick's Unity Catalog. This feature will allow users to comply with Databrick's recommendations for governing and deploying models, ensuring that all models are logged, governed, and managed in a centralized location.

Why is this use case valuable to support for MLflow users in general?

This use case is valuable for MLflow users in general because it aligns with the latest standards and recommendations for model management. By enabling MLflow Recipes to save models to the Unity Catalog, users can streamline their model governance and deployment processes, ensuring that all models are properly logged and managed. This can improve the efficiency and effectiveness of their machine learning workflows.

Why is this use case valuable to support for your project(s) or organization?

This use case is particularly valuable for my organization because our new standards require us to log and govern models through the Unity Catalog. By integrating MLflow Recipes with the Unity Catalog, we can ensure that we are adhering to these standards, improving our model governance processes and making our machine learning workflows more efficient and effective.

Why is it currently difficult to achieve this use case?

Achieving this use case is currently difficult because it requires MLflow recipes to be able to work with Unity Catalog model logging and model registry.

Details

No response

What component(s) does this bug affect?

• [ ] area/artifacts: Artifact stores and artifact logging
• [ ] area/build: Build and test infrastructure for MLflow
• [ ] area/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• [ ] area/docs: MLflow documentation pages
• [ ] area/examples: Example code
• [ ] area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• [ ] area/models: MLmodel format, model serialization/deserialization, flavors
• [X] area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• [ ] area/projects: MLproject format, project running backends
• [ ] area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• [ ] area/server-infra: MLflow Tracking server backend
• [ ] area/tracking: Tracking Service, tracking client APIs, autologging

What interface(s) does this bug affect?

• [ ] area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• [ ] area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• [ ] area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• [ ] area/windows: Windows support

What language(s) does this bug affect?

• [ ] language/r: R APIs and clients
• [ ] language/java: Java APIs and clients
• [ ] language/new: Proposals for new client languages

What integration(s) does this bug affect?

• [ ] integrations/azure: Azure and Azure ML integrations
• [ ] integrations/sagemaker: SageMaker integrations
• [ ] integrations/databricks: Databricks integrations

[daniellok-db]

Please feel free to make a contribution for this feature, the team will review it!

[github-actions[bot]]

@mlflow/mlflow-team Please assign a maintainer and start triaging this issue.