Publicação
The Feature Stores in Streamlining MLOps Workflows
| Resumo: | This 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. |
|---|---|
| Autores principais: | Nunes, Carlos |
| Outros Autores: | Ashofteh, Afshin |
| Assunto: | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1879447643712651264 |
|---|---|
| author | Nunes, Carlos |
| author2 | Ashofteh, Afshin |
| author2_role | author |
| author_facet | Nunes, Carlos Ashofteh, Afshin |
| author_role | author |
| contributor_name_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School IEEE Computer Society Press RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Nunes, Carlos\"},{\"Person.name\":\"Ashofteh, Afshin\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School IEEE Computer Society Press RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Nunes, Carlos Ashofteh, Afshin |
| datacite.date.Accepted.fl_str_mv | 2025-08-14T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| datacite.subjects.subject.fl_str_mv | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| datacite.titles.title.fl_str_mv | The Feature Stores in Streamlining MLOps Workflows |
| dc.contributor.none.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School IEEE Computer Society Press RUN |
| dc.creator.none.fl_str_mv | Nunes, Carlos Ashofteh, Afshin |
| dc.date.Accepted.fl_str_mv | 2025-08-14T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/187124 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| dc.subject.none.fl_str_mv | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| dc.title.fl_str_mv | The Feature Stores in Streamlining MLOps Workflows |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | This 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. |
| dirty | 0 |
| eu_rights_str_mv | embargoedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/6ac65fd2-ec17-4529-a3b5-4898027a42f5/download |
| funder_facet_str_mv | FCT{{{_:::_}}}Fundação para a Ciência e a Tecnologia |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.identifier_str_mv | UIDB/04152/2020 |
| funding.name_str_mv | 6817 - DCRRNI ID |
| funding_str_mv | UIDB/04152/2020 info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT |
| id | run_cf11793036656533e99b220f3402e1a1 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/187124 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/187124 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Nunes, Carlos Ashofteh, Afshin |
| publishDate | 2025 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenThis 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.application/pdfenThe Feature Stores in Streamlining MLOps WorkflowsNunes, CarlosAshofteh, AfshinNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolIEEE Computer Society PressHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf1520-9202URNIsPartOfPURE: 112447608URNIsPartOfPURE UUID: 347f16e8-7d46-4369-9b62-cde4c8f825b7URNIsPartOfScopus: 105013502879URNIsPartOfWOS: 001554235800009DOIIsPartOf10.1109/MITP.2025.35321292025-08-142025-08-14T00:00:00ZHandlehttp://hdl.handle.net/10362/187124http://purl.org/coar/access_right/c_f1cfembargoed accessMLOpsFeature StoreMachine LearningBig DataData ScienceData ManagementComplexity theoryMonitoringPredictive modelsReliabilityScalabilityStandardsThroughputTrainingWeb servicesSoftwareHardware and ArchitectureComputer Science ApplicationsSDG 9 - Industry, Innovation, and InfrastructureSDG 16 - Peace, Justice and Strong Institutions615356 bytesFundação para a Ciência e a TecnologiaInformation Management Research Centerinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PTUIDB/04152/20206817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://run.unl.pt/bitstreams/6ac65fd2-ec17-4529-a3b5-4898027a42f5/download |
| spellingShingle | The Feature Stores in Streamlining MLOps Workflows Nunes, Carlos MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| status | SINGLETON |
| subject.fl_str_mv | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| title | The Feature Stores in Streamlining MLOps Workflows |
| title_full | The Feature Stores in Streamlining MLOps Workflows |
| title_fullStr | The Feature Stores in Streamlining MLOps Workflows |
| title_full_unstemmed | The Feature Stores in Streamlining MLOps Workflows |
| title_short | The Feature Stores in Streamlining MLOps Workflows |
| title_sort | The Feature Stores in Streamlining MLOps Workflows |
| topic | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| topic_facet | MLOps Feature Store Machine Learning Big Data Data Science Data Management Complexity theory Monitoring Predictive models Reliability Scalability Standards Throughput Training Web services Software Hardware and Architecture Computer Science Applications SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions |
| url | http://hdl.handle.net/10362/187124 |
| visible | 1 |