  We utilize neural network embeddings to detect data drift by formulating the
drift detection within an appropriate sequential decision framework. This
enables control of the false alarm rate although the statistical tests are
repeatedly applied. Since change detection algorithms naturally face a tradeoff
between avoiding false alarms and quick correct detection, we introduce a loss
function which evaluates an algorithm's ability to balance these two concerns,
and we use it in a series of experiments.
