What is distributed feature computation in large-scale ML?

Distributed feature computation processes feature engineering across multiple nodes.

Systems like Spark or Flink compute features in parallel across clusters. This is required for large datasets where single-machine processing is infeasible. It involves partitioning data, aggregating results, and ensuring deterministic outputs.