  Automated Machine Learning (AutoML) has been used successfully in settings
where the learning task is assumed to be static. In many real-world scenarios,
however, the data distribution will evolve over time, and it is yet to be shown
whether AutoML techniques can effectively design online pipelines in dynamic
environments. This study aims to automate pipeline design for online learning
while continuously adapting to data drift. For this purpose, we design an
adaptive Online Automated Machine Learning (OAML) system, searching the
complete pipeline configuration space of online learners, including
preprocessing algorithms and ensembling techniques. This system combines the
inherent adaptation capabilities of online learners with the fast automated
pipeline (re)optimization capabilities of AutoML. Focusing on optimization
techniques that can adapt to evolving objectives, we evaluate asynchronous
genetic programming and asynchronous successive halving to optimize these
pipelines continually. We experiment on real and artificial data streams with
varying types of concept drift to test the performance and adaptation
capabilities of the proposed system. The results confirm the utility of OAML
over popular online learning algorithms and underscore the benefits of
continuous pipeline redesign in the presence of data drift.
