  AutoML serves as the bridge between varying levels of expertise when
designing machine learning systems and expedites the data science process. A
wide range of techniques is taken to address this, however there does not exist
an objective comparison of these techniques. We present a benchmark of current
open source AutoML solutions using open source datasets. We test auto-sklearn,
TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression
and classification datasets sourced from OpenML and find that auto-sklearn
performs the best across classification datasets and TPOT performs the best
across regression datasets.
