Comparative analysis of machine learning platforms to optimize DevOps: application of the Neutrosophic OWA-TOPSIS model - Miguel Angel Quiroz Martinez; Keyko Garces Salazar; Joshua Montesdeoca Soriano; Mónica Gomez-Rios (Neutrosophic Sets and Systems)
As software systems and their associated information become increasingly complex within DevOps environments, Machine Learning (ML) platforms are growing in importance for optimizing development and deployment processes. This article presents a comparative analysis of two leading ML platforms, Amazon Web Services (AWS) and Microsoft Azure, to evaluate their suitability for optimizing DevOps. A quantitative methodology based on an experimental comparative method was employed, applying the neutrosophic multi-criteria OWA-TOPSIS model to assess and select the best alternative based on specific criteria such as scalability, integration, performance, and cost-benefit. The results from the OWA-TOPSIS model, derived from controlled experimental assessments, indicate that Microsoft Azure offers greater advantages over AWS for DevOps optimization and software deployment in the studied use cases. However, it is acknowledged that the optimal platform choice may vary depending on the specific needs of each project and organization.