Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
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.
Cite
@article{arxiv.1907.02889,
title = {Visus: An Interactive System for Automatic Machine Learning Model Building and Curation},
author = {Aécio Santos and Sonia Castelo and Cristian Felix and Jorge Piazentin Ono and Bowen Yu and Sungsoo Hong and Cláudio T. Silva and Enrico Bertini and Juliana Freire},
journal= {arXiv preprint arXiv:1907.02889},
year = {2019}
}
Comments
Accepted for publication in the 2019 Workshop on Human-In-the-Loop Data Analytics (HILDA'19), co-located with SIGMOD 2019