Comparative Analysis of Machine Learning Platforms to Optimize DevOps - Application of the Neutrosophic OWA-TOPSIS Model

Aug 5, 2026

Software development systems and their associated information become increasingly complex in DevOps environments, increasing the need for machine learning (ML) platforms that optimize development and deployment workflows. This study compares Amazon Web Services (AWS) and Microsoft Azure using a quantitative experimental approach. A neutrosophic multi-criteria OWA-TOPSIS framework evaluates scalability, integration, performance, and cost-benefit to support platform selection. Results indicate Microsoft Azure provides stronger advantages for the studied DevOps optimization and deployment use cases, though suitability may vary by project needs and organizational context.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Background

Methodology

Experimental comparative method

Neutrosophic OWA-TOPSIS model

Results

Discussion

Conclusion

Comparative analysis of machine learning platforms to optimize DevOps: application of the Neutrosophic OWA TOPSIS model Miguel Angel Quiroz Martinez*1, Keyko Garces Salazar 2, Joshua Montesdeoca Soriano 3 and Mónica Gomez-Rios 4 1 4 GIIAR, Universidad Politécnica Salesiana, Guayaquil, [Ecuador.](Ecuador. mquiroz@ups.edu.ec)[ ](Ecuador. mquiroz@ups.edu.ec)[mquiroz@ups.edu.ec](Ecuador. mquiroz@ups.edu.ec) , [mgomezr@ups.edu.ec](mgomezr@ups.edu.ec) 2 3 Computer Science Department, Universidad Politécnica Salesiana, Guayaquil, [Ecuador.](Ecuador. kgarcess@est.ups.edu.ec)[ ](Ecuador. kgarcess@est.ups.edu.ec)[kgarcess@est.ups.edu.ec](Ecuador. kgarcess@est.ups.edu.ec) , [jmontesdeocas1@est.ups.edu.ec](jmontesdeocas1@est.ups.edu.ec) 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. Keywords: DevOps, Machine Learning, Azure, AWS, OWA-TOPSIS, Neutrosophic Sets, Single-Valued Neutrosophic Linguistic Sets, Multi-criteria Decision Making, Platform Selection 1. Introduction Software development is a common term in our daily lives due to technological advances; likewise, it is common to use software development methodologies because they help to efficiently carry out all the processes involved in software development; considering this, we have the DevOps methodology, by using this methodology it is possible to reduce the development life cycle, increasing the deployment frequency and releasing secure products that meet business requirements, thanks to the fact that the“ development ” and “operations” teams when using this methodology improve their communication and interact more frequently these two teams [1]. Machine learning platforms can be incorporated into this “DevOps” software development methodology to optimize DevOps processes since it allows the use of techniques to analyze data and logs generated during DevOps practices and automatically detect anomalies [2]. Since there is a wide variety of machine learning platforms applicable to DevOps, there is a need to determine the best platform that favors the DevOps methodology, taking into account processes such as the detection of anomalies in the data since DevOps is used, the information generated is deepened, becoming a great job to analyze the data and records generated by this practice [3]. Choosing a platform that optimizes DevOps is not something that can be taken lightly, given that the platform may not be accurate or effective enough when dealing with the data or logs; this is due to the quality and quantity of data that has been generated when using the DevOps methodology [4] . Therefore, this research seeks to analyze machine learning platforms to optimize DevOps environments through a multi-criteria comparative analysis to efficiently improve software development and deployment processes. The specific objectives are to identify machine learning platforms applicable to DevOps environments, select the main platforms through a systematic literature review, and define the crite

FAQs

Which machine learning platforms are compared to optimize DevOps?

The study compares Amazon Web Services (AWS) and Microsoft Azure as the two leading ML platforms for DevOps optimization and software deployment.

What decision-making model is used for the comparative assessment?

It applies a neutrosophic multi-criteria OWA-TOPSIS model to evaluate and select the best alternative based on defined criteria.

What criteria drive the platform selection in the OWA-TOPSIS evaluation?

The evaluation uses criteria including scalability, integration, performance, and cost-benefit derived from controlled experimental assessments.

Aug 5, 2026

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