  We argue for supplementing the process of training a prediction algorithm by
setting up a scheme for detecting the moment when the distribution of the data
changes and the algorithm needs to be retrained. Our proposed schemes are based
on exchangeability martingales, i.e., processes that are martingales under any
exchangeable distribution for the data. Our method, based on conformal
prediction, is general and can be applied on top of any modern prediction
algorithm. Its validity is guaranteed, and in this paper we make first steps in
exploring its efficiency.
