System and method for data drift detection

Abstract

Exemplary systems and methods to extract, transform, and save to memory features from a training and a test dataset at extraction layers in a machine-learning model. For each data element in the training dataset, at each extraction layer: feature maps are created and grouped by k unique data labels to construct a set of k class-conditional distributions. For each data element in the datasets: distance sets between each feature map of each extraction layer and the extraction layer's class-conditional distributions are calculated and reduced to distance summary metrics. A drift test statistic for each extraction layer is computed between the datasets by comparing the extraction layer's distance summary metric distributions of the test dataset to distance summary metric distributions of the training dataset. The measure of drift between the datasets is computed by combining the test statistics of the extraction layers through a mathematical transform.