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.
Comment: Accepted for publication in the 2019 Workshop on Human-In-the-Loop Data Analytics (HILDA'19), co-located with SIGMOD 2019
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1145/3328519.3329134 under the license http://www.acm.org/publications/policies/copyright_policy#Background
- https://arxiv.org/abs/1907.02889,
- https://arxiv.org/pdf/1907.02889.pdf,
- http://ui.adsabs.harvard.edu/abs/2019arXiv190702889S/abstract,
- https://export.arxiv.org/pdf/1907.02889,
- https://nyuscholars.nyu.edu/en/publications/visus-an-interactive-system-for-automatic-machine-learning-model-,
- https://fr.arxiv.org/abs/1907.02889,
- https://export.arxiv.org/abs/1907.02889,
- https://fr.arxiv.org/pdf/1907.02889,
- https://academic.microsoft.com/#/detail/2954438306
Document information
Published on 01/01/2019
Volume 2019, 2019
DOI: 10.1145/3328519.3329134
Licence: Other
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