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dc.contributor.authorMartín-Fernández, Cristian 
dc.contributor.authorLangendoerfer, Peter
dc.contributor.authorSoltani Zarrin, Pouya
dc.contributor.authorDíaz-Rodríguez, Manuel 
dc.contributor.authorRubio-Muñoz, Bartolomé 
dc.date.accessioned2021-08-24T06:07:52Z
dc.date.available2021-08-24T06:07:52Z
dc.date.issued2022-01-01
dc.identifier.citationFuture Generation Computer Systems, Volume 126, January 2022, Pages 15-33es_ES
dc.identifier.urihttps://hdl.handle.net/10630/22726
dc.description.abstractMachine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.es_ES
dc.description.sponsorshipThis work is funded by the Spanish projects RT2018-099777-B-100 (“rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services”), PY20_00788 (“IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration”) and UMA18FEDERJA-215 (“Advanced Monitoring System Based on Deep Learning Services in Fog”). Cristian Martín was with a postdoc grant from the Spanish project TIC-1572 (“MIsTIca: Critical Infrastructures Monitoring based on Wireless Technologies”) and his research stay at IHP has been funded through a mobility grant from the University of Malaga and IHP funding. Funding for open access charge: Universidad de Malaga/CBUA . We are grateful for the work of all the reviewers who have greatly contributed to improving the quality of this article. We would like to express our gratitude to Kai Wähner for his inspiration and ideas through numerous articles and GitHub repositories on Kafka and its combination with TensorFlow.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectInteligencia artificiales_ES
dc.subject.otherKafka-MLes_ES
dc.subject.otherApache Kafkaes_ES
dc.subject.otherMachine Learninges_ES
dc.subject.otherArtificial Intelligencees_ES
dc.subject.otherData streamses_ES
dc.subject.otherDockeres_ES
dc.subject.otherKuberneteses_ES
dc.subject.otherDistributed systemses_ES
dc.titleKafka-ML: Connecting the data stream with ML/AI frameworkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1016/j.future.2021.07.037
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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