Sara Hameed, Syed Hashim Raza Bukhari, Syeda Umm E Abiha Rizvi, Meerab Tahir, Farhan Aadil
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It critically examines the implementation of the linear regression model and its performance. Furthermore, a survey was conducted to ensure that the users know which cloud service provider is preferred. On the basis of machine learning services, people with more experience in the cloud preferred Azure Machine Learning Studio over Amazon SageMaker. Azure outperformed AWS, achieving an accuracy of 70%, whereas AWS managed only 60%. Similarly, in cases of security, storage, and pricing, Azure was preferred because of its flexible, easy-to-use services. Therefore, from the methodology, it is concluded that Azure was preferred over AWS. 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Both cloud platforms are extensive to understand, so it is difficult for users to make the right choice because of data confidentiality, better storage capacity, and, most importantly, to select a reasonable platform. This paper is an in-depth analysis of the comparable capabilities and services offered by Azure and AWS. The strengths and weaknesses of both platforms are highlighted considering security, storage, pricing, and machine learning services. It critically examines the implementation of the linear regression model and its performance. Furthermore, a survey was conducted to ensure that the users know which cloud service provider is preferred. On the basis of machine learning services, people with more experience in the cloud preferred Azure Machine Learning Studio over Amazon SageMaker. Azure outperformed AWS, achieving an accuracy of 70%, whereas AWS managed only 60%. 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A Comparative Analysis on the Services Offered by Amazon Web Services and Microsoft Azure
Different small or large-scale enterprises came to the conclusion that moving data to the cloud is more convenient. In recent decades, AWS and Azure have tried their best to provide comparatively affordable and instant solutions. Both cloud platforms are extensive to understand, so it is difficult for users to make the right choice because of data confidentiality, better storage capacity, and, most importantly, to select a reasonable platform. This paper is an in-depth analysis of the comparable capabilities and services offered by Azure and AWS. The strengths and weaknesses of both platforms are highlighted considering security, storage, pricing, and machine learning services. It critically examines the implementation of the linear regression model and its performance. Furthermore, a survey was conducted to ensure that the users know which cloud service provider is preferred. On the basis of machine learning services, people with more experience in the cloud preferred Azure Machine Learning Studio over Amazon SageMaker. Azure outperformed AWS, achieving an accuracy of 70%, whereas AWS managed only 60%. Similarly, in cases of security, storage, and pricing, Azure was preferred because of its flexible, easy-to-use services. Therefore, from the methodology, it is concluded that Azure was preferred over AWS. However, the right choice can only be made by considering the business needs.
期刊介绍:
Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of:
Parallel and distributed computing;
High-performance computing;
Computational and data science;
Artificial intelligence and machine learning;
Big data applications, algorithms, and systems;
Network science;
Ontologies and semantics;
Security and privacy;
Cloud/edge/fog computing;
Green computing; and
Quantum computing.