Proto-ML: An IDE for ML Solution Prototyping

2026-02-25

AI Summary

This work addresses the limitations of existing machine learning prototyping tools, which often lack effective support for collaboration and cross-project knowledge reuse, leading to tool fragmentation and insufficient stakeholder engagement. To overcome these challenges, the authors propose Proto-ML, an integrated development environment that unifies prototype implementation, quality evaluation, and knowledge management into three cohesive modules within a single framework. Proto-ML enables structured documentation, multi-role collaboration, and the generation of reusable artifacts. User studies demonstrate that Proto-ML significantly enhances development efficiency while fostering a more transparent and reproducible machine learning development workflow.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Prototyping plays a critical role in the development of machine learning (ML) solutions, yet existing tools often provide limited support for effective collaboration and knowledge reuse among stakeholders. This paper introduces Proto-ML, an IDE designed to strengthen ML prototyping workflows. By addressing key deficiencies such as insufficient stakeholder involvement, limited cross-project knowledge reuse, and fragmented tool support, Proto-ML offers a unified framework that enables structured documentation of prototyping activities and promotes knowledge sharing across projects. The Proto-ML IDE consists of three extension bundles: prototype implementation, analysis, and knowledge management. These extensions support tasks ranging from evaluating prototype quality against defined criteria to incorporating stakeholder perspectives throughout the development process. Preliminary user feedback suggests that Proto-ML can increase prototyping efficiency and foster more transparent and reusable ML solution development.

Problem

• ML prototyping
• collaboration
• knowledge reuse
• stakeholder involvement
• tool support

Innovation

• Proto-ML
• ML prototyping
• IDE
• knowledge reuse
• stakeholder collaboration