  Concept Drift is a phenomenon in which the underlying data distribution and
statistical properties of a target domain change over time, leading to a
degradation of the model's performance. Consequently, models deployed in
production require continuous monitoring through drift detection techniques.
Most drift detection methods to date are supervised, i.e., based on
ground-truth labels. However, true labels are usually not available in many
real-world scenarios. Although recent efforts have been made to develop
unsupervised methods, they often lack the required accuracy, have a complexity
that makes real-time implementation in production environments difficult, or
are unable to effectively characterize drift. To address these challenges, we
propose DriftLens, an unsupervised real-time concept drift detection framework.
It works on unstructured data by exploiting the distribution distances of deep
learning representations. DriftLens can also provide drift characterization by
analyzing each label separately. A comprehensive experimental evaluation is
presented with multiple deep learning classifiers for text, image, and speech.
Results show that (i) DriftLens performs better than previous methods in
detecting drift in $11/13$ use cases; (ii) it runs at least 5 times faster;
(iii) its detected drift value is very coherent with the amount of drift
(correlation $\geq 0.85$); (iv) it is robust to parameter changes.
