  Detecting drift in performance of Machine Learning (ML) models is an
acknowledged challenge. For ML models to become an integral part of business
applications it is essential to detect when an ML model drifts away from
acceptable operation. However, it is often the case that actual labels are
difficult and expensive to get, for example, because they require expert
judgment. Therefore, there is a need for methods that detect likely degradation
in ML operation without labels. We propose a method that utilizes feature space
rules, called data slices, for drift detection. We provide experimental
indications that our method is likely to identify that the ML model will likely
change in performance, based on changes in the underlying data.
