  End-to-end AutoML has attracted intensive interests from both academia and
industry, which automatically searches for ML pipelines in a space induced by
feature engineering, algorithm/model selection, and hyper-parameter tuning.
Existing AutoML systems, however, suffer from scalability issues when applying
to application domains with large, high-dimensional search spaces. We present
VolcanoML, a scalable and extensible framework that facilitates systematic
exploration of large AutoML search spaces. VolcanoML introduces and implements
basic building blocks that decompose a large search space into smaller ones,
and allows users to utilize these building blocks to compose an execution plan
for the AutoML problem at hand. VolcanoML further supports a Volcano-style
execution model - akin to the one supported by modern database systems - to
execute the plan constructed. Our evaluation demonstrates that, not only does
VolcanoML raise the level of expressiveness for search space decomposition in
AutoML, it also leads to actual findings of decomposition strategies that are
significantly more efficient than the ones employed by state-of-the-art AutoML
systems such as auto-sklearn.

Machine Learning