build-feature-store

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).

使用 Feast 构建特征存储以进行集中特征管理，配置离线和在线存储以进行批量和实时服务，通过转换定义特征视图，并为 ML 管道实现时间点正确连接。在管理多个 ML 模型的特征、确保训练服务一致性、为实时推理提供低延迟特征、跨项目重用特征定义或构建用于发现和治理的特征目录时使用。 来源：pjt222/development-guides。