Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
arXiv:1907.02889 · doi:10.1145/3328519.3329134
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.
Accepted for publication in the 2019 Workshop on Human-In-the-Loop Data Analytics (HILDA'19), co-located with SIGMOD 2019
References in corpus (4)
Cited by in corpus (7)
- 3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration
- AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models
- LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
- Auctus: A Dataset Search Engine for Data Augmentation
- PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
- Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
- Towards Evaluating Exploratory Model Building Process with AutoML Systems