Implement centralized feature management with Feast for consistent feature serving across training and inference.
Expected: Feast repository initialized with config file, sample feature definitions created, offline and online stores configured, registry path accessible.
On failure: Verify database/Redis credentials (psql -U feastuser -h localhost), check connection strings format, ensure databases exist (CREATE DATABASE featurestore), verify cloud permissions for S3/BigQuery/DynamoDB, test connectivity to storage backends, check Feast version compatibility with backends (feast version).
Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance. Source: pjt222/development-guides.