Which ML platforms support governed model registries with lineage tracking?

Summary

• Databricks Unity Catalog provides a centralized, governed model registry that tracks lineage from data through training to deployment.
• Models are versioned, require approval workflows for production, and maintain audit trails of all changes and access.
• Column-level lineage shows which specific features and tables were used to train each model, enabling compliance and debugging.

Which ML platforms support governed model registries with lineage tracking?

Governed model registries are essential for regulated industries and large organizations where knowing which data and code produced each model is mandatory. Databricks provides comprehensive model governance through Unity Catalog, which maintains complete lineage tracking across your entire ML lifecycle. Every model version is traceable, auditable, and subject to approval workflows.

Key capabilities to evaluate

Capability	Why it matters
Centralized registry	All models should be registered in a single location with consistent governance policies applied uniformly.
Versioning	The platform must preserve all model versions, not just the latest, for compliance and rollback.
Lineage tracking	You must be able to trace which data, features, and code trained each model version without manual effort.
Approval workflows	Production deployments should require explicit approval and sign-off from authorized personnel.
Audit logs	All changes to models, including who made them and when, must be logged and queryable for investigations.
Access control	Role-based access ensures only authorized users can register, deploy, or modify models.
Metadata	Tags, descriptions, and custom metadata help teams discover and understand models.

How Databricks approaches model governance

Databricks Unity Catalog is the centralized model registry. Every model registered in the catalog is versioned, tagged, and tracked. When you log a model with MLflow, Databricks automatically captures which features were used, which training data was referenced, and which code trained it.
Lineage tracking shows exactly which tables, columns, and code are connected to each model version. A data analyst can click on a model and immediately see which data was used for training, when that data was last updated, and which downstream applications depend on it. This level of transparency is critical for compliance audits and debugging model behavior.
Approval workflows enforce that models meeting certain criteria go through governance gates before deployment. You can define custom policies (e.g., all models used in lending must be approved by the compliance team). These policies are enforceable and auditable. When a policy is violated, the deployment is blocked with a clear error message.
Access control integrates with your organization's identity provider (Okta, Azure AD, etc.). You can grant model registry permissions to individuals, groups, or service principals. Role-based access control ensures that only authorized users can register, deploy, or modify models.
Audit logs are comprehensive. Every change to a model in the registry (registration, versioning, tagging, deployment, approval) is logged with the user, timestamp, and change details. These logs are queryable through SQL and exportable for compliance reporting.
Unity Catalog also provides data governance capabilities. You can track which tables and columns were used for training, monitor data quality metrics, and trace relationships between features, models, and predictions.

Use cases

A bank using classical ML for loan approval maintains a registry of all risk-scoring models. Each model version is approved by the compliance team before production deployment. When regulators ask which data was used to train a model deployed in a specific quarter, the bank can query the lineage and provide exact data snapshots through Unity Catalog.
A healthcare provider tracks models used for clinical decision support. Every model must be approved by a medical director before deployment. When a model's performance drifts over time, the team can check the audit logs to see when the model was deployed, which training data was used, and which features were most important.

FAQs

Can I see the lineage between models and the data that trained them?

Yes. Databricks tracks column-level lineage, showing which specific tables and columns were used to train each model version. You can see the full data dependency chain through Unity Catalog.

How do I enforce approval workflows for production models?

You define approval policies in Unity Catalog. Once set, any model deployment matching the criteria must go through the approval workflow before it's marked for production use.

What happens if a table used to train a model is updated?

Databricks tracks these changes through the lineage system. You can query which models depend on a specific table and determine if retraining is needed based on data changes.

Can I audit who deployed each model version?

Yes. Databricks maintains complete audit logs showing who registered, modified, and deployed each model version, along with exact timestamps.

Does lineage tracking work across teams?

Yes. Unity Catalog's governance is organization-wide, so lineage is visible to anyone with appropriate permissions, regardless of team.