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dc.contributor.authorVänskä, Sini
dc.contributor.authorKemell, Kai-Kristian
dc.contributor.authorMikkonen, Tommi
dc.contributor.authorAbrahamsson, Pekka
dc.date.accessioned2024-01-10T06:45:10Z
dc.date.available2024-01-10T06:45:10Z
dc.date.issued2024
dc.identifier.citationVänskä, S., Kemell, K.-K., Mikkonen, T., & Abrahamsson, P. (2024). Continuous Software Engineering Practices in AI/ML Development Past the Narrow Lens of MLOps : Adoption Challenges. <i>E-Informatica</i>, <i>18</i>(1), 240102. <a href="https://doi.org/10.37190/e-Inf240102" target="_blank">https://doi.org/10.37190/e-Inf240102</a>
dc.identifier.otherCONVID_194848990
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/92624
dc.description.abstractBackground: Continuous software engineering practices are currently considered state of the art in Software Engineering (SE). Recently, this interest in continuous SE has extended to ML system development as well, primarily through MLOps. However, little is known about continuous SE in ML development outside the specific continuous practices present in MLOps. Aim: In this paper, we explored continuous SE in ML development more generally, outside the specific scope of MLOps. We sought to understand what challenges organizations face in adopting all the 13 continuous SE practices identified in existing literature. Method: We conducted a multiple case study of organizations developing ML systems. Data from the cases was collected through thematic interviews. The interview instrument focused on different aspects of continuous SE, as well as the use of relevant tools and methods. Results: We interviewed 8 ML experts from different organizations. Based on the data, we identified various challenges associated with the adoption of continuous SE practices in ML development. Our results are summarized through 7 key findings. Conclusion: The largest challenges we identified seem to stem from communication issues. ML experts seem to continue to work in silos, detached from both the rest of the project and the customers.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherPolitechnika Wroclawska Oficyna Wydawnicza
dc.relation.ispartofseriesE-Informatica
dc.rightsCC BY 4.0
dc.subject.otherartificial intelligence
dc.subject.othermachine learning
dc.subject.othercontinuous software engineering
dc.subject.othercontinuous star
dc.subject.othermultiple case study
dc.titleContinuous Software Engineering Practices in AI/ML Development Past the Narrow Lens of MLOps : Adoption Challenges
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202401101125
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange240102
dc.relation.issn1897-7979
dc.relation.numberinseries1
dc.relation.volume18
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 The Authors. Published by Wrocław University of Science and Technology Publishing House.
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysotekoäly
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.37190/e-Inf240102
dc.type.okmA1


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