[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117007-en":3,"doc-seo-117007-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117007,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Empowering Machine Learning Development with Service-Oriented Computing Principles - Paper abstract and framework proposal","Service-oriented computing (SOC) has streamlined software development, but machine learning (ML) development has not fully integrated these practices. The gap stems from ML-specific development challenges and the lack of a unified approach to incorporate services into ML workflows. This paper analyzes the mismatch between SOC and ML development and introduces “Everything as a Module” (XaaM), mapping ML artifacts—models, code, data, and configurations—into modules. It also outlines additional steps and an architecture to manage and orchestrate complex ML systems.","University of Groningen  \nEmpowering Machine Learning Development with Service-Oriented Computing Principles  \nYousefi, Mostafa Hadadian Nejad; Degeler, Viktoriya; Lazovik, Alexander  \nPublished in:  \nService-Oriented Computing  \nDOI:  \n10. 1007/978-3-031-45728-9_2  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nYousefi, M. H. N. , Degeler, V. , & Lazovik, A. (2023) . Empowering Machine Learning Development  \nwith Service-Oriented Computing Principles. In M. Aiello, J. Barzen, S. Dustdar, & F. Leymann (Eds.), Service-Oriented Computing: 17th Symposium and Summer School, SummerSOC 2023 Heraklion, Crete, Greece, June 25 – July 1, 2023 (pp. 24-44) . (Communications in Computer and Information Science; Vol. 1847) . Springer. [https://doi.org/10.1007/978-3-031-45728-9_2](https://doi.org/10.1007/978-3-031-45728-9_2)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nEmpowering Machine Learning Development with Service-Oriented Computing Principles  \nMostafa Hadadian Nejad Yousefi 1 , Viktoriya Degeler2 , and Alexander Lazovik 1(B)  \n1 Faculty of Science and Engineering, Bernoulli Institute, University of Groningen,  \nGroningen, The Netherlands  \n{m.hadadian,[a.lazovik}@rug.nl](a.lazovik}@rug.nl)  \n2 Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam,  \nThe Netherlands  \n[v.o.degeler@uva.nl](v.o.degeler@uva.nl)  \nAbstract. Despite software industries’ successful utilization of ServiceOriented Computing (SOC) to streamline software development, machine learning (ML) development has yet to fully integrate these practices. This disparity can be attributed to multiple factors, such as the unique challenges inherent to ML development and the absence of a unified framework for incorporating services into this process. In this paper, we shed light on the disparities between services-oriented computing and machine learning development. We propose “Everything as a Module” (XaaM), a framework designed to encapsulate every ML artifacts including models, code, data, and configurations as individual modules, to bridge this gap. We propose a set of additional steps that need to betaken to empower machine learning development using services-oriented computing via an architecture that facilitates efficient management and orchestration of complex ML systems. By leveraging the best practices of services-oriented computing, we believe that machine learning development can achieve a higher level of maturity, improve the efficiency of ","cbCaicbbuUR2iFbE","https://ap.wps.com/l/cbCaicbbuUR2iFbE","pdf",641636,1,22,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Production-grade ML challenges\n## Gap between SOC and ML development\n## DevOps and collaboration in software delivery","[{\"question\":\"Why is machine learning development not fully adopting service-oriented computing best practices?\",\"answer\":\"ML development still lacks full integration because of ML-specific development challenges and the absence of a unified framework for incorporating services into the ML process.\"},{\"question\":\"What framework does the paper propose to bridge the gap?\",\"answer\":\"The paper proposes “Everything as a Module” (XaaM), which encapsulates ML artifacts—including models, code, data, and configurations—as individual modules.\"},{\"question\":\"How does the proposed approach help manage complex ML systems?\",\"answer\":\"It includes additional steps and an architecture designed to facilitate efficient management and orchestration of complex ML systems, improving development maturity and efficiency.\"}]","Empowering Machine Learning Development with Service-Oriented Computing Principles - 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