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While the demand for machine learning (ML) applications is booming, there isa scarcity of data scientists capable of building such models. Automaticmachine learning (AutoML) approaches have been proposed that help with thisproblem by synthesizing end-to-end ML data processing pipelines. However, thesefollow a best-effort approach and a user in the loop is necessary to curate andrefine the derived pipelines. Since domain experts often have little or noexpertise in machine learning, easy-to-use interactive interfaces that guidethem throughout the model building process are necessary. In this paper, wepresent Visus, a system designed to support the model building process andcuration of ML data processing pipelines generated by AutoML systems. Wedescribe the framework used to ground our design choices and a usage scenarioenabled by Visus. Finally, we discuss the feedback received in user testingsessions with domain experts.

Visus: An Interactive System for Automatic Machine Learning Model Building and Curation