Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping Study

Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping Study Kouider Chadli, Goetz Botterweck, Takfarinas Saber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4694122/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The widespread adoption of Machine Learning (ML) across various sectors presents unique challenges beyond the scope of conventional software engineering, especially throughout the lifecycle of ML-Enabled Systems (MLES). As ML becomes central to software operations, the substantial computational resources required for their training, testing, retraining, and maintenance underscore the urgent need for sustainable DevOps practices in AI-centric software ecosystems. Despite the critical importance of this subject, there remains a lack of a unified review that addresses these sustainability challenges within the ML lifecycle from a holistic perspective. This study aims to bridge the research gap by conducting a systematic mapping study of current practices and methodologies that promote sustainable MLOps. Additionally, we have mapped these techniques and methodologies across the MLOps pipeline and extracted lessons learned from each phase to enhance our understanding and application of sustainable practices in MLOps. By doing so, this paper seeks to offer insights into strategies that could mitigate the environmental, economic, technical, social, and individual sustainability challenges associated with MLES, thereby contributing to the development of more sustainable ML-Enabled Systems. Sustainability DevOps MLOps Machine Learning-Enabled Systems Systematic Mapping Study Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4694122","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329607249,"identity":"908001dd-7d26-476f-acbf-f69736410293","order_by":0,"name":"Kouider Chadli","email":"data:image/png;base64,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","orcid":"","institution":"University of Galway","correspondingAuthor":true,"prefix":"","firstName":"Kouider","middleName":"","lastName":"Chadli","suffix":""},{"id":329607250,"identity":"c5391d40-fbc5-4a72-9d2e-dac1b9b2cdcd","order_by":1,"name":"Goetz Botterweck","email":"","orcid":"","institution":"Trinity College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Goetz","middleName":"","lastName":"Botterweck","suffix":""},{"id":329607253,"identity":"3506a6c9-f060-4685-8517-f103d52f76db","order_by":2,"name":"Takfarinas Saber","email":"","orcid":"","institution":"University of Galway","correspondingAuthor":false,"prefix":"","firstName":"Takfarinas","middleName":"","lastName":"Saber","suffix":""}],"badges":[],"createdAt":"2024-07-05 20:27:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4694122/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4694122/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92034469,"identity":"b838ca6b-1c4a-4fde-b6a3-4af8e9a7b07d","added_by":"auto","created_at":"2025-09-23 23:46:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":865868,"visible":true,"origin":"","legend":"","description":"","filename":"KouiderREMEDYSustainableDevOpsMLES.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4694122/v1_covered_4b745acb-3a3d-45c3-b3d3-57d81d30907a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainability, DevOps, MLOps, Machine Learning-Enabled Systems, Systematic Mapping Study","lastPublishedDoi":"10.21203/rs.3.rs-4694122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4694122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The widespread adoption of Machine Learning (ML) across various sectors presents unique challenges beyond the scope of conventional software engineering, especially throughout the lifecycle of ML-Enabled Systems (MLES). 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