Centralized Feature Store

Centralized feature management accelerated ML experimentation and deployment across teams.

Key Takeaways:

Fragmented features slowed ML delivery

Machine learning teams lacked a unified system to manage features, slowing experimentation and deployment.

We centralized feature ownership and access

A shared feature store was introduced to support consistent reuse and faster iteration across teams.

Databricks feature store architecture

The feature store was built on Databricks using a medallion architecture, with Bronze, Silver, and Gold layers owned by different teams to balance governance and velocity.

Model deployment cycles reduced from 80 to 20 days

Faster feature access significantly accelerated experimentation and production deployment.

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Governed Metrics for AI

Single logic across every interface

Compliance Reporting

Reporting automated

Audience Segmentation

Targeting efficiency improved