How do you design clustering pipelines with feature store integration?

Feature store integration ensures consistent feature retrieval for clustering across training and inference.

Feature stores act as centralized systems for storing and serving ML features. In clustering pipelines, features are pulled from the store to ensure consistency between training and inference. This prevents training-serving skew. Feature versioning ensures reproducibility, while offline/online sync ensures real-time updates.