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The pipeline for the continuous development of artificial intelligence models-Current state of research and practice
University Innsbruck, Austria..ORCID iD: 0000-0002-3410-7637
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-3818-4442
Software Competence Center Hagenberg GmbH SCCH, Austria..
2023 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 199, article id 111615Article, review/survey (Refereed) Published
Abstract [en]

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required.This paper includes a Multivocal Literature Review (MLR) where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for Development and Operations (DevOps) and Continuous Integration (CI)/Continuous Delivery (CD) for AI, Machine Learning Operations (MLOps), (end-to-end) lifecycle management, and Continuous Delivery for Machine Learning (CD4ML). Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.(c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 199, article id 111615
Keywords [en]
Continuous development of AI, Continuous (end-to-end) lifecycle pipeline for AI, MLOps, CI, CD for AI, DevOps for AI, Multivocal literature review
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-24504DOI: 10.1016/j.jss.2023.111615ISI: 000967982100001OAI: oai:DiVA.org:bth-24504DiVA, id: diva2:1755645
Available from: 2023-05-09 Created: 2023-05-09 Last updated: 2023-05-09Bibliographically approved

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Felderer, Michael

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