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

arXiv: https://arxiv.org/abs/1907.02889 · HTML: https://mlsanity.com/paper/1907.02889/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 9 of 9)

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- [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025)
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- [Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations](https://mlsanity.com/paper/2307.04036/) (2023)
- [Interactive Data Harmonization with LLM Agents: Opportunities and Challenges](https://mlsanity.com/paper/2502.07132/) (2025)
- [AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models](https://mlsanity.com/paper/2101.11970/) (2021)
- [3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration](https://mlsanity.com/paper/2401.15877/) (2024)
- [Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variables](https://mlsanity.com/paper/2510.25974/) (2025)

## Abstract (arXiv)

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.