How do you design fault-tolerant clustering pipelines in production ML systems?

Fault-tolerant clustering pipelines use checkpointing, retries, and idempotent computations.

Production ML pipelines must survive failures in data ingestion, compute nodes, or storage. Clustering jobs are designed to be idempotent so reruns produce consistent results. Checkpointing intermediate centroids allows recovery without restarting full computation. Distributed orchestration tools manage retries and job recovery.