  This paper elucidates the importance of governing an artificial intelligence
model post-deployment and overseeing potential fluctuations in the distribution
of present data in contrast to the training data. The concepts of data drift
and concept drift are explicated, along with their respective foundational
distributions. Furthermore, a range of metrics is introduced, which can be
utilized to scrutinize the model's performance concerning potential temporal
variations.
