  Research progress in AutoML has lead to state of the art solutions that can
cope quite wellwith supervised learning task, e.g., classification with
AutoSklearn. However, so far thesesystems do not take into account the changing
nature of evolving data over time (i.e., theystill assume i.i.d. data); even
when this sort of domains are increasingly available in realapplications (e.g.,
spam filtering, user preferences, etc.). We describe a first attempt to
de-velop an AutoML solution for scenarios in which data distribution changes
relatively slowlyover time and in which the problem is approached in a lifelong
learning setting. We extendAuto-Sklearn with sound and intuitive mechanisms
that allow it to cope with this sort ofproblems. The extended Auto-Sklearn is
combined with concept drift detection techniquesthat allow it to automatically
determine when the initial models have to be adapted. Wereport experimental
results in benchmark data from AutoML competitions that adhere tothis scenario.
Results demonstrate the effectiveness of the proposed methodology.
