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dc.contributor.authorHanchuk, Danylo O.-
dc.contributor.authorSemerikov, Serhiy O.-
dc.date.accessioned2026-07-11T08:24:07Z-
dc.date.available2026-07-11T08:24:07Z-
dc.date.issued2025-02-07-
dc.identifier.citationAutomating machine learning: A meta-synthesis of MLOps tools, frameworks and architectures / Danylo O. Hanchuk, Serhiy O. Semerikov // Proceedings of the 7th Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2024). Virtual Event, Kryvyi Rih, Ukraine, December 27, 2024 / Edited by: Serhiy O. Semerikov, Andrii M. Striuk // CEUR Workshop Proceedings. – 2025. – Vol. 3917. – P. 362-414. – Access mode : https://ceur-ws.org/Vol-3917/paper60.pdf DOI: https://doi.org/10.55056/ceur-ws.org/Vol-3917/paper60.pdfuk_UA
dc.identifier.isbnhttps://doi.org/10.55056/ceur-ws.org/Vol-3917/paper60.pdf-
dc.identifier.issn1613-0073-
dc.identifier.urihttps://ceur-ws.org/Vol-3917/paper60.pdf-
dc.identifier.urihttp://ds.knu.edu.ua/jspui/handle/123456789/9264-
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dc.description.abstractAutomating the end-to-end lifecycle of machine learning models is critical for their effective operationalization. Various tools, frameworks and architectures have emerged to support Machine Learning Operations (MLOps) practices. This paper presents a meta-synthesis of existing reviews to provide a comprehensive overview of such enabling technologies for MLOps. The capabilities and features offered by common commercial and open-source MLOps platforms are compared. Patterns in the MLOps architecture and design philosophies are identified. The role of containers, orchestration, configuration management, and infrastructure automation in ML pipelines is examined. Approaches for model deployment on cloud and edge are also discussed. The synthesis offers insights for tool selection and usage to automate enterprise-scale machine learning.uk_UA
dc.language.isoenuk_UA
dc.publisherCEUR Workshop Proceedingsuk_UA
dc.subjectMLOpsuk_UA
dc.subjectautomationuk_UA
dc.subjecttoolsuk_UA
dc.subjectframeworksuk_UA
dc.subjectarchitectureuk_UA
dc.subjectmodel deploymentuk_UA
dc.subjectML pipelinesuk_UA
dc.subjectmeta-synthesisuk_UA
dc.titleAutomating machine learning: A meta-synthesis of MLOps tools, frameworks and architecturesuk_UA
dc.typeArticleuk_UA
local.submitter.emailsemerikov@ccjourn...uk_UA
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