[r/ML] [R] How are you managing long-running preprocessing jobs at scale? Curious what's actually working

Summary

A small ML team is struggling with managing long-running, large-scale data preprocessing jobs (50-100GB datasets) that take hours and are prone to painful failures. They find existing orchestration tools like Prefect and Temporal too complex, requiring dedicated DevOps resources that their model-focused team lacks. This highlights a common challenge for many ML teams in efficiently handling data pipelines without extensive infrastructure support.

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