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

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2019-07-05 DOI:10.1145/3328519.3329134

Aécio Santos, Sonia Castelo, Cristian Felix, Jorge Piazentin Ono, Bowen Yu, S. Hong, Cláudio T. Silva, E. Bertini, J. Freire

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引用次数: 24

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.

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Visus:一个用于自动机器学习模型构建和管理的交互式系统

虽然对机器学习(ML)应用程序的需求正在蓬勃发展，但能够构建此类模型的数据科学家却很稀缺。已经提出了自动机器学习(AutoML)方法，通过综合端到端的ML数据处理管道来帮助解决这个问题。然而，这些都遵循尽力而为的方法，并且循环中的用户是必要的，以管理和改进派生的管道。由于领域专家通常很少或没有机器学习方面的专业知识，因此在整个模型构建过程中指导他们的易于使用的交互界面是必要的。在本文中，我们介绍了Visus，一个旨在支持AutoML系统生成的ML数据处理管道的模型构建过程和管理的系统。我们描述了用于设计选择的框架和Visus支持的使用场景。最后，我们与领域专家讨论用户测试会话中收到的反馈。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)

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