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Machine Learning (ML) research has primarily focused on improving the accuracy and efficiency of the training algorithms while paying much less attention to the equally important problem of understanding, validating, and monitoring the data fed to ML. Irrespective of the ML algorithms used, data errors can adversely affect the quality of the generated model. This indicates that we need to adopt a data-centric approach to ML that treats data as a first-class citizen, on par with algorithms anddoi:10.1145/3318464.3384707 dblp:conf/sigmod/CavenessCPP0Z20 fatcat:agjc4n4f5jgw3kfmatmmvcueeu