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The Feature Stores in Streamlining MLOps Workflows

2025-08-14Artigo científico Acesso embargado
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dc.contributor.authorNunes, Carlos
dc.contributor.authorAshofteh, Afshin
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblIEEE Computer Society Press
dc.date.accessioned2025-08-28T21:51:32Z
dc.date.embargoedUntil2027-08-14
dc.date.issued2025-08-14
dc.descriptionNunes, C., & Ashofteh, A. (2025). The Feature Stores in Streamlining MLOps Workflows. IT Professional, 27(4), 40-47. https://doi.org/10.1109/MITP.2025.3532129 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
dc.description.abstractThis article studies the significance of feature stores in machine learning (ML) operations frameworks, specifically examining how they affect processing times and overall workflow efficiency. Feature stores act as centralized storage locations for handling and providing features for ML models, thus guaranteeing uniformity and dependability throughout training and inference steps. We used the Freddie Mac Single-Family Loan-Level dataset to examine the differences in processing times when utilizing a feature store compared to not using one. The findings show that feature stores effectively improve ML processes by decreasing delays and boosting output, which is crucial for fast model development and prediction. Moreover, the article puts forward a structure for assessing the advantages of incorporating a feature store, considering elements like team size, feature intricacy, and the requirement for feature supervision as well as challenges such as the complexity of integration and infrastructure costs.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent615356
dc.identifier.doi10.1109/MITP.2025.3532129
dc.identifier.issn1520-9202
dc.identifier.otherPURE: 112447608
dc.identifier.otherPURE UUID: 347f16e8-7d46-4369-9b62-cde4c8f825b7
dc.identifier.otherScopus: 105013502879
dc.identifier.otherWOS: 001554235800009
dc.identifier.urihttp://hdl.handle.net/10362/187124
dc.identifier.urlhttps://www.scopus.com/pages/publications/105013502879
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001554235800009
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationInformation Management Research Center
dc.subjectMLOps
dc.subjectFeature Store
dc.subjectMachine Learning
dc.subjectBig Data
dc.subjectData Science
dc.subjectData Management
dc.subjectComplexity theory
dc.subjectMonitoring
dc.subjectPredictive models
dc.subjectReliability
dc.subjectScalability
dc.subjectStandards
dc.subjectThroughput
dc.subjectTraining
dc.subjectWeb services
dc.subjectSoftware
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.subjectSDG 16 - Peace, Justice and Strong Institutions
dc.titleThe Feature Stores in Streamlining MLOps Workflowsen
dc.typejournal article
degois.publication.firstPage40
degois.publication.issue4
degois.publication.lastPage47
degois.publication.titleIT Professional
degois.publication.volume27
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsembargoedAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)