# 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.

### Continue Reading

### [Governed Metrics for AI](/content/case-studies/semantic-layer-enterprise-metrics/index.html)

Single logic across every interface

### [Compliance Reporting](/content/case-studies/compliance-reporting/index.html)

Reporting automated

### [Audience Segmentation](/content/case-studies/audience-segmentation/index.html)

Targeting efficiency improved