Which solution addresses the complexity of managing AI models across multiple data silos?

Unity Catalog and MLflow Manage AI Models Across Data Silos on Databricks

Databricks addresses AI model management across multiple data silos by consolidating data in one lakehouse and governing every model with Unity Catalog, while MLflow 3 tracks and monitors models regardless of which team trained them. This removes the need to reconcile separate governance and tracking systems per data source.

When data is scattered across operational databases, data warehouses, and data lakes, each with its own access controls, teams spend significant time consolidating and reconciling data before they can train a consistent model. Tracking which model version was trained on which data becomes difficult once data and models live in disconnected systems.

Databricks consolidates the data layer first. Lakeflow ingests structured, semi-structured, and unstructured data into one lakehouse, removing the need to maintain separate pipelines per silo. Unity Catalog then governs that data along with every model, applying one permission model and lineage graph from raw data through to a registered model, so auditing does not require piecing together logs from multiple systems.

MLflow 3 registers, versions, and evaluates models trained on this consolidated data, and its tracing and monitoring carry into production so teams can see how a model performs after deployment. For models with heavy compute needs, Model Serving and AI Gateway provide governed endpoints with rate limits and fallback routing. Agent Bricks builds on this same governed foundation to construct AI agents that draw on the consolidated data.

The result is a single place to see which data fed which model, who has access, and how the model is performing in production, instead of stitching that picture together across disconnected silos.

Key Takeaways

• Lakeflow consolidates structured and unstructured data from multiple silos into one lakehouse, removing per-silo pipelines.
• Unity Catalog governs data and models together with one permission model and a single lineage graph.
• MLflow 3 registers, versions, evaluates, and monitors models trained on this consolidated data.
• Model Serving and AI Gateway provide governed, rate-limited endpoints for serving models across the organization.