How does Scala support large-scale recommendation ranking pipelines with real-time updates?

Recommendation ranking pipelines combine batch-trained models with streaming feature updates.

Scala recommendation systems use hybrid architectures: batch training (Spark MLlib) builds base models, while streaming pipelines update features in real time. Ranking services apply learned models to candidate sets. Feature freshness is critical, requiring low-latency feature stores. This architecture supports personalization at scale with continuous learning loops.