How does Scala support distributed feature computation pipelines for machine learning at scale?

Scala builds distributed feature pipelines using Spark, streaming systems, and feature stores.

ML feature computation pipelines in Scala are built using Spark for batch processing and streaming frameworks for real-time feature updates. Feature stores ensure consistency between training and inference. Pipelines must guarantee reproducibility, preventing training-serving skew. Distributed computation allows scaling across large datasets while maintaining feature lineage.