Learning to Validate the Predictions of Black Box Classifiers on Unseen Data

Machine Learning (ML) models are difficult to maintain in production settings. In particular, deviations of the unseen serving data (for which we want to compute predictions) from the source data (on which the model was trained) pose a central challenge, especially when model training and prediction are outsourced via cloud services. Errors or shifts in the serving data can affect the predictive quality of a model, but are hard to detect for engineers operating ML deployments.