Distributed Feature Store Design for Machine Learning Pipelines with Versioned Data Synchronization

Abstract

Distributed feature stores must keep training features, online serving values, transformation definitions, and entity keys synchronized across machine learning pipelines. This article presents a versioned synchronization design that connects a feature registry, offline store, online store, freshness monitor, and serving validator into one controlled feature delivery layer. The design prevents unsafe feature publication by checking schema compatibility, feature version, entity-key alignment, timestamp validity, and freshness state before values move into online serving. The findings show that version-aware synchronization improves feature freshness, strengthens offline-online consistency, and reduces training-inference mismatch compared with simpler feature-store deployment modes. The analysis also shows that longer synchronization intervals increase the risk of stale serving values, version drift, and feature skew. These outcomes indicate that distributed feature stores require governed synchronization logic rather than simple offline-to-online copying.