  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.
