International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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India | Data Knowledge Engineering | Volume 8 Issue 8, August 2019 | Pages: 2326 - 2329


Comparative Study of Cloud Providers (AWS, Azure, Google Cloud) using Artificial Intelligence with DevOps

Naresh Lokiny

Abstract: The rapid advancement of cloud computing has revolutionized how businesses operate, offering scalable resources and cutting - edge technologies. This paper presents a comparative study of the three leading cloud providers?Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) - focused on their integration of Artificial Intelligence (AI) with DevOps practices. We evaluate their AI service offerings, DevOps toolchains, and the seamless integration of these technologies to enhance automation, scalability, and reliability. The analysis provides insights into the strengths and weaknesses of each provider, helping organizations make informed decisions on their cloud strategy. By analyzing the AI capabilities, DevOps toolchain integration, and overall performance of these cloud providers, this study aims to provide insights into selecting the most suitable platform for AI - driven DevOps workflows. The comparative analysis will focus on factors such as AI services, scalability, automation capabilities, and integration with DevOps tools to help organizations make informed decisions when choosing a cloud provider for their AI and DevOps initiatives.

Keywords: Cloud Providers, AWS, Azure, Google Cloud, Artificial Intelligence, DevOps, Comparative Study, AI Services, DevOps Integration, Scalability, Automation

How to Cite?: Naresh Lokiny, "Comparative Study of Cloud Providers (AWS, Azure, Google Cloud) using Artificial Intelligence with DevOps", Volume 8 Issue 8, August 2019, International Journal of Science and Research (IJSR), Pages: 2326-2329, https://www.ijsr.net/getabstract.php?paperid=SR24724151213, DOI: https://dx.doi.org/10.21275/SR24724151213


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