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date:: 4/2023
issn:: 00076813
doi:: 10.1016/j.bushor.2023.04.003
title:: @Democratizing artificial intelligence: How no-code AI can leverage machine learning operations
pages:: S0007681323000502
item-type:: [[journalArticle]]
access-date:: 2023-05-17T09:30:33Z
original-title:: Democratizing artificial intelligence: How no-code AI can leverage machine learning operations
language:: en
url:: https://linkinghub.elsevier.com/retrieve/pii/S0007681323000502
short-title:: Democratizing artificial intelligence
publication-title:: Business Horizons
journal-abbreviation:: Business Horizons
authors:: [[Leif Sundberg]], [[Jonny Holmström]]
library-catalog:: DOI.org (Crossref)
links:: [Local library](zotero://select/library/items/GL2TZIAY), [Web library](https://www.zotero.org/users/1039502/items/GL2TZIAY)
- [[Abstract]]
- Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) such as machine learning (ML) systems into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article we outline three key challenges and discuss how an emerging form of AI platforms no-code AI may help organizations to address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining important remaining challenges associated with no-code AI and MLOps we propose three managerial Journal Pre-proof recommendations. By doing so, we provide insights into an important novel, emerging phenomenon in AI software and set the stage for further research in the area.
- [[Attachments]]
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