Batch inference: how do you design scalable offline scoring pipelines?

Batch inference scores many entities on a schedule. Design points: - Partition data and parallelize compute - Ensure idempotent outputs (upserts) - Track model version per score - Monitor runtime, cost, and failures Use Spark/Beam for large jobs and keep outputs consistent with online features when you have both paths.