What is distributed feature computation architecture in classification systems?

Distributed feature computation processes feature generation across multiple nodes for scalability.

Large-scale classification systems compute features using distributed frameworks like Spark, Flink, or Beam. Data is partitioned across nodes, and transformations are applied in parallel. This is essential for handling terabytes of data efficiently. Systems must ensure consistency, fault tolerance, and deterministic feature generation.