Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos

ilustraciones, diagramas

Autores:: Mendez Aguirre, Oscar Alexander

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
dc.title.translated.eng.fl_str_mv	Machine Learning Operations applied to the process of model development and provisioning
title	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
spellingShingle	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Machine Learning MLOps DevSecOps Gestión de datos Innovación tecnológica Desarrollo de software Machine Learning MLOps DevSecOps Security Data management Technological innovation Software development Aprendizaje automático Integridad de datos Gestión de datos machine learning data integrity data management
title_short	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
title_full	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
title_fullStr	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
title_full_unstemmed	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
title_sort	Machine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelos
dc.creator.fl_str_mv	Mendez Aguirre, Oscar Alexander
dc.contributor.advisor.spa.fl_str_mv	Camargo Mendoza, Jorge Eliécer Flórez Fernández, Héctor Arturo
dc.contributor.author.spa.fl_str_mv	Mendez Aguirre, Oscar Alexander
dc.contributor.researchgroup.spa.fl_str_mv	UnSecureLab
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Machine Learning MLOps DevSecOps Gestión de datos Innovación tecnológica Desarrollo de software Machine Learning MLOps DevSecOps Security Data management Technological innovation Software development Aprendizaje automático Integridad de datos Gestión de datos machine learning data integrity data management
dc.subject.proposal.spa.fl_str_mv	Machine Learning MLOps DevSecOps Gestión de datos Innovación tecnológica Desarrollo de software
dc.subject.proposal.eng.fl_str_mv	Machine Learning MLOps DevSecOps Security Data management Technological innovation Software development
dc.subject.wikidata.spa.fl_str_mv	Aprendizaje automático Integridad de datos Gestión de datos
dc.subject.wikidata.eng.fl_str_mv	machine learning data integrity data management
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-05-06T20:27:00Z
dc.date.available.none.fl_str_mv	2024-05-06T20:27:00Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
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dc.relation.references.spa.fl_str_mv	Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Maddox, W., Maarek, Y., McDirmid, S., New, M., et al. (2022). Software engineering for machine learning: An experience report. IEEE Software, 39(5):68–75. Banerjee, A., Chen, C.-C., Hung, C.-C., Huang, X., Wang, Y., and Chevesaran, R. (2020). Challenges and experiences with mlops for performance diagnostics in hybrid-cloud enter- prise software deployments. Baylor, D., Haas, K., Katsiapis, K., Leong, S., Liu, R., Menwald, C., Miao, H., Polyzotis, N., Trott, M., and Zinkevich, M. (2019). Continuous training for production tMLu in the tTensorFlowu extended (tttttTFXuuuuu) platform. In 2019 USENIX Conference on Operational Machine Learning (OpML 19), pages 51–53. Benjumea, J., Ropero, J., Rivera, J., Mavrogiannopoulos, N., and Kamhoua, C. (2022). Catalog of requirements for artificial intelligence products. Future Internet, 14(5):118. Bodor, A., Hnida, M., and Najima, D. (2023). MLOps: Overview of Current State and Future Directions, volume 629 LNNS. Chatterjee, A., Ahmed, B., Hallin, E., and Engman, A. (2022a). Quality assurance in mlops setting: An industrial perspective. volume 3362. Chatterjee, A., Ahmed, B. S., Hallin, E., and Engman, A. (2022b). Quality assurance in mlops setting: An industrial perspective. arXiv preprint arXiv:2211.12706. Díaz, J., Pérez, J. E., Lopez-Peña, M. A., Mena, G. A., and Yagüe, A. (2019). Self-service cybersecurity monitoring as enabler for devsecops. Ieee Access, 7:100283–100295. DrivenData (2023). Cookiecutter data science. https://drivendata.github.io/cookiecutter- data-science/. Último acceso: [Fecha de último acceso]. Eck, B., Kabakci-Zorlu, D., Chen, Y., Savard, F., and Bao, X. (2022). A monitoring frame- work for deployed machine learning models with supply chain examples. arXiv preprint arXiv:2211.06239. Fujii, T. Y., Hayashi, V. T., Arakaki, R., Ruggiero, W. V., Bulla Jr, R., Hayashi, F. H., and Khalil, K. A. (2021). A digital twin architecture model applied with mlops techniques to improve short-term energy consumption prediction. Machines, 10(1):23. Gartner (2016). Gartner Says Business Intelligence and Analytics Leaders Must Focus on Mindsets and Culture to Kick Start Advanced Analytics. Technical report, Gartner. Garzas, J. and Piattini, M. (2022). Failure factors in machine learning projects. In Advances in Intelligent Systems and Computing, volume 1197, pages 21–31. Springer. Ghanta, S., Subramanian, S., Khermosh, L., Sundararaman, S., Shah, H., Goldberg, Y., Roselli, D., and Talagala, N. (2019). Ml health monitor: Taking the pulse of machine learning algorithms in production. volume 11139. Google Cloud (2022). Mlops: Continuous delivery and automation pipelines in machine learning. Gärtler, M., Khaydarov, V., Klöpper, B., and Urbas, L. (2021). The machine learning life cycle in chemical operations – status and open challenges. Chemie-Ingenieur-Technik, 93:2063–2080. Hernandez, J., Daza, K., and Florez, H. (2022). Spiking neural network approach based on caenorhabditis elegans worm for classification. IAENG International Journal of Computer Science, 49(4). Hewage, N. and Meedeniya, D. (2022). Machine learning operations: A survey on mlops tool support. arXiv. Humble, J. and Kim, G. (2018). Accelerate: The science of lean software and devops: Building and scaling high performing technology organizations. IT Revolution. John, M., Olsson, H., and Bosch, J. (2021). Towards mlops: A framework and maturity model. pages 334–341. Kitchenham, B. and Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and software technology, 55(12):2049–2075. Kreuzberger, D., Kühl, N., and Hirschl, S. (2023). Machine learning operations (mlops): Overview, definition, and architecture. IEEE Access. Lim, J., Lee, H., Won, Y., and Yeon, H. (2019). Mlop lifecycle scheme for vision-based inspection process in manufacturing. pages 9–11. Lima, A., Monteiro, L., and Furtado, A. (2022). Mlops: Practices, maturity models, roles, tools, and challenges - a systematic literature review. volume 1, pages 308–320. Liu, L. T., Wang, S., Britton, T., and Abebe, R. (2023). Reimagining the machine learning life cycle to improve educational outcomes of students. Proceedings of the National Academy of Sciences, 120(9):e2204781120. Makinen, S., Skogstrom, H., Laaksonen, E., and Mikkonen, T. (2021). Who needs mlops: What data scientists seek to accomplish and how can mlops help? pages 109–112. Malhotra, Y. (2022). How you can implement well-architected ‘zero trust’hybrid-cloud com- puting beyond ‘lift and shift’: cloud-enabled digital innovation at scale with infrastructure as code (iac), devsecops and mlops. In 2022 New York State Cyber Security Conference: Invited Presentations, Albany, New York: https://its. ny. gov/2022-nyscsc. Martel, Y., Roßmann, A., Sultanow, E., Weiß, O., Wissel, M., Pelzel, F., and Seßler, M. (2021). Software architecture best practices for enterprise artificial intelligence. INFOR- MATIK 2020. Mboweni, T., Masombuka, T., and Dongmo, C. (2022). A systematic review of machine learning devops. In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6. IEEE. Mejía, J. A. G. and González, F. (2022). Encuesta nacional “machine learning operations y sus desafíos de implementación en colombia”. Revista Sistemas, (165):20–26. Mirza, B., Li, X., Lauwers, K., Reddy, B., Muller, A., Wozniak, C., and Djali, S. (2023). A clinical site workload prediction model with machine learning lifecycle. Healthcare Analytics, 3:100159. Parihar, A. S., Gupta, U., Srivastava, U., Yadav, V., and Trivedi, V. K. (2023). Automa- ted machine learning deployment using open-source ci/cd tool. In Proceedings of Data Analytics and Management: ICDAM 2022, pages 209–222. Springer. Paul, S. K., Riaz, S., and Das, S. (2022). A conceptual architecture for ai in supply chain risk management. In TENCON 2022-2022 IEEE Region 10 Conference (TENCON), pages 1–5. IEEE. Recupito, G., Pecorelli, F., Catolino, G., Moreschini, S., Nucci, D., Palomba, F., and Tam- burri, D. (2022). A multivocal literature review of mlops tools and features. pages 84–91. Reddy, M., Dattaprakash, B., Kammath, S., Kn, S., Manokaran, S., and Be, R. (2022). Application of mlops in prediction of lifestyle diseases. ECS Transactions, 107(1):1191. Robertson, J. and Robertson, S. (2000). Volere. Requirements Specification Templates. Saeed, W. and Omlin, C. (2023). Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, page 110273. Schulz, C., Gao, J., and Sun, T. (2020). Mlops: A literature review. In 2020 IEEE 1st International Workshop on Machine Learning in Business Process Management (BPML). IEEE. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., and Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28. Treveil, M., Omont, N., Stenac, C., Lefevre, K., Phan, D., Zentici, J., Lavoillotte, A., Miya- zaki, M., and Heidmann, L. (2020). Introducing MLOps. O’Reilly Media. Yasar, H. (2020). Leveraging devops and devsecops to accelerate ai development and deploy- ment. CARNEGIEMELLON UNIV PITTSBURGH PA PITTSBURGH United States. Zhang, X. and Jaskolka, J. (2022). Conceptualizing the secure machine learning operations (secmlops) paradigm. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), pages 127–138. IEEE. Zhou, Y., Yu, Y., and Ding, B. (2020). Towards mlops: A case study of ml pipeline platform. pages 494–500.
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dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
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dc.format.extent.spa.fl_str_mv	xiv, 103 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Camargo Mendoza, Jorge Eliécerac7a39b905a0f361c4925b472819b8f0Flórez Fernández, Héctor Arturo4d5f31513bff25f046a942247fc4b5a6Mendez Aguirre, Oscar Alexander5b3a22c8d3c21488522977a851bddc4dUnSecureLab2024-05-06T20:27:00Z2024-05-06T20:27:00Z2024https://repositorio.unal.edu.co/handle/unal/86035Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEn la actual era de la ingeniería de software, donde el Machine Learning (ML) desempeña un papel crucial en la innovación tecnológica, la aplicación efectiva de prácticas de desarrollo y operación es esencial. El enfoque de DevSecOps (Development Security Operations) se ha popularizado por su capacidad para integrar la seguridad y la calidad en todas las etapas del ciclo de vida del desarrollo seguro de software. Sin embargo, en el contexto específico del Machine Learning, surge la necesidad de un enfoque especializado que considere las particula- ridades de los modelos y algoritmos utilizados. El Machine Learning Operations (MLOps), a pesar de su relativa novedad, busca establecer un marco para caracterizar el ciclo de vida del desarrollo de ML, desacoplarlo del desarrollo de software y garantizar atributos de calidad como escalabilidad, mantenibilidad y seguridad. También se enfrenta al desafío de gestionar datos de entrenamiento, la seguridad en el proceso de análisis y desarrollo de modelos, y la necesidad de una cultura orientada a la calidad. Este trabajo se centra en investigar cómo la implementación de MLOps puede impactar positivamente en la gestión del ciclo de vida del desarrollo de ML, con el objetivo de contribuir al conocimiento en este campo emergente y promover la adopción de las mejores prácticas en soluciones basadas en ML. (Texto tomado de la fuente).In the current era of software engineering, where Machine Learning (ML) plays a pivotal role in technological innovation, the effective implementation of development and opera- tions practices is essential. The DevSecOps (Development Security Operations) approach has gained popularity due to its ability to integrate security and quality at every stage of the software development lifecycle. However, in the specific context of Machine Learning, there arises a need for a specialized approach that takes into account the peculiarities of the models and algorithms used. Machine Learning Operations (MLOps), despite its relative immaturity, aims to establish a framework for characterizing the ML development lifecycle, decoupling it from software development, and ensuring quality attributes such as scalability, maintainability, and security. It also grapples with challenges related to managing training data, security throughout the model analysis, development and deployment process, and the need for a quality-oriented culture. This thesis focuses on investigating how the implementa- tion of MLOps can positively impact the management of the ML development lifecycle, with the goal of contributing to knowledge in this emerging field and promoting the adoption of best practices in ML-based solutions.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónAplicaciones del machine learning operationsxiv, 103 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónMachine LearningMLOpsDevSecOpsGestión de datosInnovación tecnológicaDesarrollo de softwareMachine LearningMLOpsDevSecOpsSecurityData managementTechnological innovationSoftware developmentAprendizaje automáticoIntegridad de datosGestión de datosmachine learningdata integritydata managementMachine Learning Operations aplicado al proceso de desarrollo y aprovisionamiento de modelosMachine Learning Operations applied to the process of model development and provisioningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAmershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Maddox, W., Maarek, Y., McDirmid, S., New, M., et al. (2022). Software engineering for machine learning: An experience report. IEEE Software, 39(5):68–75.Banerjee, A., Chen, C.-C., Hung, C.-C., Huang, X., Wang, Y., and Chevesaran, R. (2020). Challenges and experiences with mlops for performance diagnostics in hybrid-cloud enter- prise software deployments.Baylor, D., Haas, K., Katsiapis, K., Leong, S., Liu, R., Menwald, C., Miao, H., Polyzotis, N., Trott, M., and Zinkevich, M. (2019). Continuous training for production tMLu in the tTensorFlowu extended (tttttTFXuuuuu) platform. In 2019 USENIX Conference on Operational Machine Learning (OpML 19), pages 51–53.Benjumea, J., Ropero, J., Rivera, J., Mavrogiannopoulos, N., and Kamhoua, C. (2022). Catalog of requirements for artificial intelligence products. Future Internet, 14(5):118.Bodor, A., Hnida, M., and Najima, D. (2023). MLOps: Overview of Current State and Future Directions, volume 629 LNNS.Chatterjee, A., Ahmed, B., Hallin, E., and Engman, A. (2022a). Quality assurance in mlops setting: An industrial perspective. volume 3362.Chatterjee, A., Ahmed, B. S., Hallin, E., and Engman, A. (2022b). Quality assurance in mlops setting: An industrial perspective. arXiv preprint arXiv:2211.12706.Díaz, J., Pérez, J. E., Lopez-Peña, M. A., Mena, G. A., and Yagüe, A. (2019). Self-service cybersecurity monitoring as enabler for devsecops. Ieee Access, 7:100283–100295.DrivenData (2023). Cookiecutter data science. https://drivendata.github.io/cookiecutter- data-science/. Último acceso: [Fecha de último acceso].Eck, B., Kabakci-Zorlu, D., Chen, Y., Savard, F., and Bao, X. (2022). A monitoring frame- work for deployed machine learning models with supply chain examples. arXiv preprint arXiv:2211.06239.Fujii, T. Y., Hayashi, V. T., Arakaki, R., Ruggiero, W. V., Bulla Jr, R., Hayashi, F. H., and Khalil, K. A. (2021). A digital twin architecture model applied with mlops techniques to improve short-term energy consumption prediction. Machines, 10(1):23.Gartner (2016). Gartner Says Business Intelligence and Analytics Leaders Must Focus on Mindsets and Culture to Kick Start Advanced Analytics. Technical report, Gartner.Garzas, J. and Piattini, M. (2022). Failure factors in machine learning projects. In Advances in Intelligent Systems and Computing, volume 1197, pages 21–31. Springer.Ghanta, S., Subramanian, S., Khermosh, L., Sundararaman, S., Shah, H., Goldberg, Y., Roselli, D., and Talagala, N. (2019). Ml health monitor: Taking the pulse of machine learning algorithms in production. volume 11139.Google Cloud (2022). Mlops: Continuous delivery and automation pipelines in machine learning.Gärtler, M., Khaydarov, V., Klöpper, B., and Urbas, L. (2021). The machine learning life cycle in chemical operations – status and open challenges. Chemie-Ingenieur-Technik, 93:2063–2080.Hernandez, J., Daza, K., and Florez, H. (2022). Spiking neural network approach based on caenorhabditis elegans worm for classification. IAENG International Journal of Computer Science, 49(4).Hewage, N. and Meedeniya, D. (2022). Machine learning operations: A survey on mlops tool support. arXiv.Humble, J. and Kim, G. (2018). Accelerate: The science of lean software and devops: Building and scaling high performing technology organizations. IT Revolution.John, M., Olsson, H., and Bosch, J. (2021). Towards mlops: A framework and maturity model. pages 334–341.Kitchenham, B. and Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and software technology, 55(12):2049–2075.Kreuzberger, D., Kühl, N., and Hirschl, S. (2023). Machine learning operations (mlops): Overview, definition, and architecture. IEEE Access.Lim, J., Lee, H., Won, Y., and Yeon, H. (2019). Mlop lifecycle scheme for vision-based inspection process in manufacturing. pages 9–11.Lima, A., Monteiro, L., and Furtado, A. (2022). Mlops: Practices, maturity models, roles, tools, and challenges - a systematic literature review. volume 1, pages 308–320.Liu, L. T., Wang, S., Britton, T., and Abebe, R. (2023). Reimagining the machine learning life cycle to improve educational outcomes of students. Proceedings of the National Academy of Sciences, 120(9):e2204781120.Makinen, S., Skogstrom, H., Laaksonen, E., and Mikkonen, T. (2021). Who needs mlops: What data scientists seek to accomplish and how can mlops help? pages 109–112.Malhotra, Y. (2022). How you can implement well-architected ‘zero trust’hybrid-cloud com- puting beyond ‘lift and shift’: cloud-enabled digital innovation at scale with infrastructure as code (iac), devsecops and mlops. In 2022 New York State Cyber Security Conference: Invited Presentations, Albany, New York: https://its. ny. gov/2022-nyscsc.Martel, Y., Roßmann, A., Sultanow, E., Weiß, O., Wissel, M., Pelzel, F., and Seßler, M. (2021). Software architecture best practices for enterprise artificial intelligence. INFOR- MATIK 2020.Mboweni, T., Masombuka, T., and Dongmo, C. (2022). A systematic review of machine learning devops. In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6. IEEE.Mejía, J. A. G. and González, F. (2022). Encuesta nacional “machine learning operations y sus desafíos de implementación en colombia”. Revista Sistemas, (165):20–26.Mirza, B., Li, X., Lauwers, K., Reddy, B., Muller, A., Wozniak, C., and Djali, S. (2023). A clinical site workload prediction model with machine learning lifecycle. Healthcare Analytics, 3:100159.Parihar, A. S., Gupta, U., Srivastava, U., Yadav, V., and Trivedi, V. K. (2023). Automa- ted machine learning deployment using open-source ci/cd tool. In Proceedings of Data Analytics and Management: ICDAM 2022, pages 209–222. Springer.Paul, S. K., Riaz, S., and Das, S. (2022). A conceptual architecture for ai in supply chain risk management. In TENCON 2022-2022 IEEE Region 10 Conference (TENCON), pages 1–5. IEEE.Recupito, G., Pecorelli, F., Catolino, G., Moreschini, S., Nucci, D., Palomba, F., and Tam- burri, D. (2022). A multivocal literature review of mlops tools and features. pages 84–91.Reddy, M., Dattaprakash, B., Kammath, S., Kn, S., Manokaran, S., and Be, R. (2022). Application of mlops in prediction of lifestyle diseases. ECS Transactions, 107(1):1191.Robertson, J. and Robertson, S. (2000). Volere. Requirements Specification Templates.Saeed, W. and Omlin, C. (2023). Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, page 110273.Schulz, C., Gao, J., and Sun, T. (2020). Mlops: A literature review. In 2020 IEEE 1st International Workshop on Machine Learning in Business Process Management (BPML). IEEE.Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., and Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28.Treveil, M., Omont, N., Stenac, C., Lefevre, K., Phan, D., Zentici, J., Lavoillotte, A., Miya- zaki, M., and Heidmann, L. (2020). Introducing MLOps. O’Reilly Media.Yasar, H. (2020). Leveraging devops and devsecops to accelerate ai development and deploy- ment. CARNEGIEMELLON UNIV PITTSBURGH PA PITTSBURGH United States.Zhang, X. and Jaskolka, J. (2022). Conceptualizing the secure machine learning operations (secmlops) paradigm. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), pages 127–138. IEEE.Zhou, Y., Yu, Y., and Ding, B. (2020). Towards mlops: A case study of ml pipeline platform. pages 494–500.Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86035/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL80121552.2024.pdf80121552.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf6999757https://repositorio.unal.edu.co/bitstream/unal/86035/2/80121552.2024.pdf418f3b55646b3b907e86ab56a45e791dMD52THUMBNAIL80121552.2024.pdf.jpg80121552.2024.pdf.jpgGenerated Thumbnailimage/jpeg4335https://repositorio.unal.edu.co/bitstream/unal/86035/3/80121552.2024.pdf.jpged9e8604fb37b297691751e4e487c83aMD53unal/86035oai:repositorio.unal.edu.co:unal/860352024-05-06 23:04:44.398Repositorio Institucional Universidad Nacional de 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