Build Feature Store

See Extended Examples for complete configuration files and templates.

Implement centralized feature management with Feast for consistent feature serving across training and inference.

When to Use

• Managing features for multiple ML models across teams
• Ensuring training-serving consistency for features
• Implementing point-in-time correct historical features
• Serving low-latency features for real-time inference
• Reusing feature definitions across projects
• Versioning feature transformations
• Building feature catalog for discovery and governance
• Preventing feature leakage in training pipelines

Inputs