Visus

Paper: https://dl.acm.org/doi/abs/10.1145/3328519.3329134

Abstract: While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-to-end ML data processing pipelines. However, these follow a best-effort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.

Visus’s data selection and problem definition screens: (A) select or load dataset view, (B) select task view (create a new problem, or load an previously created problem), (C) define problem view (displays data summaries and allows selection of problem parameters), (D) configure search view (allows setup of additional model search parameters).