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