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An artificial intelligence life cycle: From conception to production

This paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the chal...

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Detalles Bibliográficos
Autores principales: De Silva, Daswin, Alahakoon, Damminda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214328/
https://www.ncbi.nlm.nih.gov/pubmed/35755876
http://dx.doi.org/10.1016/j.patter.2022.100489
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author De Silva, Daswin
Alahakoon, Damminda
author_facet De Silva, Daswin
Alahakoon, Damminda
author_sort De Silva, Daswin
collection PubMed
description This paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases—design, develop, and deploy—and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.
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spelling pubmed-92143282022-06-23 An artificial intelligence life cycle: From conception to production De Silva, Daswin Alahakoon, Damminda Patterns (N Y) Descriptor This paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases—design, develop, and deploy—and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article. Elsevier 2022-04-13 /pmc/articles/PMC9214328/ /pubmed/35755876 http://dx.doi.org/10.1016/j.patter.2022.100489 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Descriptor
De Silva, Daswin
Alahakoon, Damminda
An artificial intelligence life cycle: From conception to production
title An artificial intelligence life cycle: From conception to production
title_full An artificial intelligence life cycle: From conception to production
title_fullStr An artificial intelligence life cycle: From conception to production
title_full_unstemmed An artificial intelligence life cycle: From conception to production
title_short An artificial intelligence life cycle: From conception to production
title_sort artificial intelligence life cycle: from conception to production
topic Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214328/
https://www.ncbi.nlm.nih.gov/pubmed/35755876
http://dx.doi.org/10.1016/j.patter.2022.100489
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