Collaborative automated machine learning (AutoML) process framework

Johnas Camillius Chami () and Vitor Santos ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 7675-7685

Abstract: In the face of rapid technological advancements and digital disruption, Small and Medium Enterprises (SMEs) grapple with integrating data-driven practices essential for competitiveness and growth. Unlike large corporations, SMEs often lack the resources and technical expertise to implement sophisticated data analytics and machine learning solutions. This study addresses the identified gap by developing a Collaborative Automated Machine Learning (AutoML) Process Framework tailored to the unique needs of SMEs. Leveraging Design Science Research methodology, the research conceptualizes, designs, and validates an accessible AutoML tool that automates complex machine learning processes while fostering collaboration among stakeholders. The framework aims to democratize advanced analytics, enabling SMEs to harness domain knowledge and drive data-driven decision-making without extensive data science expertise. The findings demonstrate that the proposed collaborative AutoML framework significantly enhances SMEs' operational efficiency, decision-making capabilities, and competitive edge, thereby contributing to their digital transformation and broader economic growth. This research not only bridges the existing gap in AutoML applications for SMEs but also aligns with sustainable development goals by promoting inclusive innovation and economic resilience.

Keywords: Automated machine learning (AutoML); Collaborative framework; Data-driven transformation; Design science research; Digital transformation; Small and medium enterprises (SMEs). (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/3676/1374 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:8:y:2024:i:6:p:7675-7685:id:3676

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().