  The rapid advancement of models based on artificial intelligence demands
innovative monitoring techniques which can operate in real time with low
computational costs. In machine learning, especially if we consider artificial
neural networks (ANNs), the models are often trained in a supervised manner.
Consequently, the learned relationship between the input and the output must
remain valid during the model's deployment. If this stationarity assumption
holds, we can conclude that the ANN provides accurate predictions. Otherwise,
the retraining or rebuilding of the model is required. We propose considering
the latent feature representation of the data (called "embedding") generated by
the ANN to determine the time when the data stream starts being nonstationary.
In particular, we monitor embeddings by applying multivariate control charts
based on the data depth calculation and normalized ranks. The performance of
the introduced method is compared with benchmark approaches for various ANN
architectures and different underlying data formats.
