2026-06-01T05:15:01Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/364402026-02-03T11:01:42Zcom_10630_2254col_10630_37953

Burgueño Romero, Antonio Manuel Benítez-Hidalgo, Antonio Barba-González, Cristóbal Aldana-Montes, José Francisco 2025-01-16T13:56:51Z 2025-01-16T13:56:51Z 2024-07-08 A. M. Burgueño-Romero, A. Benítez-Hidalgo, C. Barba-González and J. F. Aldana-Montes, "Toward an Open Source MLOps Architecture," in IEEE Software, vol. 42, no. 1, pp. 59-64, Jan.-Feb. 2025, doi: 10.1109/MS.2024.3421675 https://hdl.handle.net/10630/36440 10.1109/MS.2024.3421675 The need for robust MLOps frameworks has become paramount in a world increasingly reliant on AI. Current solutions range from proprietary cloud services to independent open-source components, each with advantages and limitations. This paper presents a Kubernetes-based, open-source MLOps framework designed to streamline the lifecycle management of machine learning models in production environments. It integrates a comprehensive suite of open-source tools compatible with Python, covering all aspects from development, testing, deployment, and monitoring to updating models, reducing the need for human intervention. Finally, we compared state-of-the-art MLOps tools and frameworks, demonstrating that our framework meets the same features as proprietary options, such as Amazon SageMaker. eng http://creativecommons.org/licenses/by-nc/4.0/ open access Atribución-NoComercial 4.0 Internacional Soporte lógico libre Towards an open-source MLOps architecture journal article