How does Scala support large-scale real-time personalization systems?

Real-time personalization uses streaming features, user profiles, and ML inference pipelines.

Scala systems combine streaming ingestion (Kafka), feature computation (Spark/Flink), and real-time inference engines. User behavior is continuously updated into feature stores. Models are served via low-latency APIs. Event-driven pipelines ensure personalization updates happen within milliseconds to seconds.