SCALING AN ML INFERENCE PIPELINE FOR BATCH WORKLOADS

Summary from the TLDR Data newsletter

WHOOP cut a 15.8-million-task internal ML simulation from more than two months to six days by removing per-task HTTP calls, loading the model in workers, and tuning pod CPU. It also documents why Python fork deadlocked native libraries, how spawn fixed it, and how SQS chain state plus atomic S3 writes made retries idempotent.