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Debugging Data Science workflows at scale

May 12, 2023 15 minute read #python, #dask, #kubernetes, #apache-beam, #google-cloud, #google-kubernetes-engine

The more we scale up our workloads the more we run into bugs that only appear at scale. Reproducing these bugs can be expensive, time consuming and error prone. In order to report a bug on a GitHub repo you generally need to isolate the bug and come up with a minimal reproducer so that the maintainer can investigate. But what if a minimal reproducer requires hundreds of servers to isolate and replicate?

Debugging Data Science workflows at scale