Medium's feature store data model lessons

Medium's feature store became a bottleneck as the data model grew complex. Engineers learned that denormalizing features and separating online from offline storage improved performance. The team also adopted a feature registry to enforce schema consistency and reduce debugging time across ML pipelines.