SYSTEMS AND METHODS FOR DETECTING DRIFT BETWEEN DATA USED TO TRAIN A MACHINE LEARNING MODEL AND DATA USED TO EXECUTE THE MACHINE LEARNING MODEL

In some embodiments, a first plurality of representations are extracted from a first data set. A first set of distributions are generated based on the first plurality of representations. A machine learning model is trained based on the first plurality of representations and the first set of distributions. A second plurality of representations are extracted from a second data set different from the first data set. The machine learning model is executed based on the second plurality of representations to produce a second set of distributions. An anomaly score is determined for each datum from the second data set to produce a set of anomaly scores. The set of anomaly scores are determined based on the first set of distributions and the second set of distributions. A notification is generated when at least one anomaly score from the set of anomaly scores is larger than a predetermined threshold.