The discussion highlights a significant gap in the data preparation stage of machine learning, often overlooked by the ML community. While benchmarks exist for feature engineering and algorithm tuning, the database community has long understood the importance of data cleaning and integration. Increased collaboration between these two fields is essential to enhance productivity and systematically improve data preparation processes.
