  Machine learning models nowadays play a crucial role for many applications in
business and industry. However, models only start adding value as soon as they
are deployed into production. One challenge of deployed models is the effect of
changing data over time, which is often described with the term concept drift.
Due to their nature, concept drifts can severely affect the prediction
performance of a machine learning system. In this work, we analyze the effects
of concept drift in the context of a real-world data set. For efficient concept
drift handling, we introduce the switching scheme which combines the two
principles of retraining and updating of a machine learning model. Furthermore,
we systematically analyze existing regular adaptation as well as triggered
adaptation strategies. The switching scheme is instantiated on New York City
taxi data, which is heavily influenced by changing demand patterns over time.
We can show that the switching scheme outperforms all other baselines and
delivers promising prediction results.
