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Publicación Estandarización y automatización del ciclo de vida de modelos de Machine learning en entornos empresariales mediante prácticas de MLOps(Universidad EAFIT, 2025) Escobar Diaz, Juan Esteban; Montoya Múnera, Edwin NelsonCurrently, the development and deployment of Machine learning (ML) models in enterprise environments face significant challenges related to reproducibility, scalability, and process automation. The absence of clear standards for managing the ML model lifecycle can lead to operational inefficiencies, data governance issues, and integration difficulties with production systems. This work proposes the implementation of an architecture based on a structured methodology following MLOps practices, aiming to standardize the development, deployment, and monitoring of ML models within the organization. The proposed methodology and architecture integrate modern tools and approaches to automate the ML pipeline, manage model versioning, enable production monitoring, and optimize computational resource usage.