# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 4 Results and Discussion
### Final Comparative Result  
We can summarize the best platform for each metric in Table 8 below. As in Table 8, when comparing Azure and AWS, it is evident that each platform excels in specific areas. AWS boasts a superior upload time, making it an excellent choice for users who prioritize efficient data transfer from their local environment to the cloud. On \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **AWS** & **Azure** \\ \hline Upload latency & ✓ & \\ Download latency & & ✓ \\ Inference time & & ✓ \\ Throughput & & ✓ \\ Cost & ✓ & \\ ML service catalog & ✓ & \\ Ease of deployment & ✓ & \\ Service-level agreement & ✓ & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of comparison of each platform across all metrics. the other hand, Azure stands out with its superior download time, ensuring the swift and seamless retrieval of data from the cloud to the user's local environment. When it comes to inference time and throughput, Azure succeeds. The CPU cluster on Azure is optimized to provide slightly faster inference times, enabling the quicker processing of machine learning models and real-time decision making. Azure's higher throughput capabilities allow for the handling of larger volumes of data, ensuring smooth and efficient data-processing workflows. On the other hand, the AWS cost structure is generally considered to be more affordable. This affordability makes it an attractive option for businesses looking to optimize their cloud expenses. Furthermore, AWS holds an advantage in terms of its catalog of machine learning resources. The platform offers an extensive array of prebuilt machine learning models, frameworks, and tools, providing users with a wide range of options to meet their specific needs. This robust availability of ML resources on AWS can significantly benefit users who require a diverse set of capabilities for their machine learning projects. In addition, the ease of the deployment process on AWS is more seamless and developer friendly, making it an excellent choice for the rapid development of ML services. Finally, AWS offers a slight advantage in terms of its SLA, allowing customers to experience minimal downtime and maximum connectivity for their ML applications. Ultimately, the choice between Azure and AWS depends on the specific requirements and priorities of the user or organization. Azure's strengths in download time, inference time, and throughput make it a compelling option for those seeking efficient data transfer and processing. Meanwhile, AWS's advantage in upload time, cost, catalog of ML resources, ease of deployment, and SLA caters well to users looking for comprehensive machine learning capabilities. It is essential to evaluate these factors and consider the specific needs and objectives before selecting the most suitable cloud platform. Since both platforms have their merits and demerits, it is more useful to categorize them based on the business requirements and suggest the best platform for each. Based on the empirical results obtained and the discussion above, we can now identify the top eight common business requirements as inspired by [29,30] and conclude the best platform for each requirement, as in Table 9 below. Table 9 can be used as a guide to aid decision making on the suitable platform based on business-requirement importance. For more code-focused developers, AWS is more suitable since Azure offers more \"no-code\"/\"low-code\" alternatives and ML workflows, while AWS allows for easier integration through APIs and cloud notebooks that aide development, as in Section 4.7. Moreover, training, developing, and deploying custom models is simpler in AWS since Amazon SageMaker provides a highly flexible and wide range of services that can be used in different stages in an ML pipeline. This amalgamates to provide an overall higher level of customization at any level of granularity, as discussed in Section 4.6. Additionally, as shown by the analysis in Section 4.5, AWS was more cost effective than Azure.