  Optimizing a machine learning pipeline for a task at hand requires careful
configuration of various hyperparameters, typically supported by an AutoML
system that optimizes the hyperparameters for the given training dataset. Yet,
depending on the AutoML system's own second-order meta-configuration, the
performance of the AutoML process can vary significantly. Current AutoML
systems cannot automatically adapt their own configuration to a specific use
case. Further, they cannot compile user-defined application constraints on the
effectiveness and efficiency of the pipeline and its generation. In this paper,
we propose CAML, which uses meta-learning to automatically adapt its own AutoML
parameters, such as the search strategy, the validation strategy, and the
search space, for a task at hand. The dynamic AutoML strategy of CAML takes
user-defined constraints into account and obtains constraint-satisfying
pipelines with high predictive performance.
