Online AutoML consists of an exploration phase followed by an exploitation phase, efficient in environments where the working conditions change over time.

We study online optimization methods for hyper-parameter tuning. In dynamic environments, the “optimal” hyper-parameters might change over time. The exploration phase is looking to find the set of hyper-parameters for the current working condition. The exploitation phase continuously monitors the learning process to detect degradation in the performance of the system which triggers a new exploitation phase. We consider complex problems described by pipelines where each step in the pipeline has its own hyper-parameters. We consider problems with many hyper-parameters where some of them might be irrelevant. Among the relevant parameters, the complexity of the model architecture (with particular reference to deep networks) is of particular relevance and was the objective of our study.