Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
This addresses the scarcity of data scientists by providing an easy-to-use tool for domain experts, though it is incremental as it builds on existing AutoML approaches.
The paper tackles the problem of domain experts lacking machine learning expertise by introducing Visus, an interactive system that supports building and curating AutoML-generated pipelines, with user testing feedback indicating its utility.
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.