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Machine learning is a powerful tool for gleaning knowledge from massive amounts of data. While a great deal of machine learning research has focused on improving the accuracy and efficiency of training and inference algorithms, there is less attention in the equally important problem of monitoring the quality of data fed to machine learning. The importance of this problem is hard to dispute: errors in the input data can nullify any benefits on speed and accuracy for training and inference. This argument points to a data-centric approach to machine learning that treats training and serving data as an important production asset, on par with the algorithm and infrastructure used for learning.