How does R support enterprise-grade feature store architecture for machine learning systems?

R integrates with feature stores via external systems that centralize, version, and serve features consistently across training and inference.

Feature stores decouple feature engineering from model training. In R-based architectures, feature computation is often done in batch (targets, Spark, or data.table pipelines) and stored in systems like Feast or custom data warehouses. The key architectural principle is feature consistency between training and serving. R acts as a consumer of feature APIs rather than owning feature computation runtime in production.