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This article covers the top 7 data engineering gotchas in an ML project. The list is sorted in descending order based on the number of times I have encountered the issue multiplied by the impact of each occurrence on the overall project. This article is a subset from a broader list of "98 things that can go wrong in an. ML projects are a team sport involving Data Engineers, Data Scientists, Statisticians, DataOps / MLOps engineers, Business Domain experts, Business domain experts.