Comparative analysis of machine learning platforms to op-timize DevOps: application of the Neutrosophic OWA-TOPSIS model

Authors

Miguel Angel Quiroz Martinez, GIIAR, Universidad Politécnica Salesiana, Guayaquil, EcuadorFollow
Keyko Garces Salazar, Computer Science Department, Universidad Politécnica Salesiana, Guayaquil, EcuadorFollow
Joshua Montesdeoca Soriano, Computer Science Department, Universidad Politécnica Salesiana, Guayaquil, EcuadorFollow
Mónica Gomez-Rios, GIIAR, Universidad Politécnica Salesiana, Guayaquil, EcuadorFollow

Abstract

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.

Recommended Citation

Quiroz Martinez, Miguel Angel; Keyko Garces Salazar; Joshua Montesdeoca Soriano; and Mónica Gomez-Rios. "Comparative analysis of machine learning platforms to op-timize DevOps: application of the Neutrosophic OWA-TOPSIS model." Neutrosophic Sets and Systems 84, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol84/iss1/32