# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 1 Introduction  
Cloud computing is a rapidly growing field and has applications in virtually all domains. With the advent of various cloud-computing platforms, developers and cloud architects need to compare these providers to identify the most viable platform for every specific use case and application. Based on these use cases, each cloud-provider platform offers unique advantages over its competitors in terms of the services available, quantitative metrics, integrations, and tools available. This project aims to compare and contrast two popular cloud-providing platforms, namely Microsoft Azure and Amazon Web Services (AWS). The primary goal of this project is to gain insights into the viability of the two most popular cloud platforms in terms of the deployment of machine learning (ML) applications, in particular, object-detection algorithms. This comparison allows developers to easily identify the best choice between these providers, which could have an extensive impact on their core project requirements such as performance and development costs. The benefits of comparing cloud platforms for machine learning model deployment are numerous. In today's dynamic technological landscape, selecting the right cloud platform to deploy machine learning models is a critical decision that can significantly impact thesuccess of projects. The choice between cloud service providers, such as Azure and AWS, becomes increasingly challenging as developers and data scientists strive to meet specific project requirements. A thorough comparison of these platforms is crucial to empower practitioners with the insights needed to make informed decisions. Nevertheless, these benefits extend beyond mere technical considerations. The suitability of a cloud provider directly influences the performance, scalability, and cost effectiveness of machine learning applications. By conducting a comprehensive analysis, developers can align their choice with the unique demands of their projects, ultimately optimizing the deployment process. This approach is particularly pertinent in the realm of machine learning, where nuances in infrastructure, tooling, and support for specialized algorithms can significantly impact outcomes. This paper addresses a significant gap in the literature by  
* providing a comprehensive comparison of the cloud-provider platforms based on quantitative and qualitative metrics via both experimentation and theory,
* suggesting the most suitable platform based on the importance of different factors to allow developers to make a more-informed choice on the best platform for their needs. Quantitative metrics, rooted in experimental data, provide a tangible and objective basis for comparison. Metrics such as upload and download times, throughput, and inference times offer insights into the raw performance capabilities of each platform. However, a complete understanding of a cloud provider's suitability goes beyond these metrics. Qualitative aspects, evaluated theoretically, delve into the broader ecosystem, considering factors like cost effectiveness, the availability of machine learning resources, and ease of deployment. While quantitative metrics offer a precise measurement of performance, qualitative aspects introduce a holistic perspective, encompassing the user experience and the adaptability of the platform to diverse project requirements. A theoretical evaluation allows for a nuanced understanding of each platform's capabilities, reflecting on their documentation, community support, and strategic direction. Combining both experimental and theoretical approaches ensures a well-rounded assessment that caters to the multifaceted needs of machine learning practitioners. This paper examines quantitative metrics such as upload and download times, throughput, and inference time, as well as qualitative metrics such as cost effectiveness, the availability of machine learning resources, ease of deployment, and service-level agreements (SLAs).