VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

Understand

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.