# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 5 Conclusions  
In conclusion, this paper addressed a significant gap in the existing literature by conducting a comprehensive comparison of two prominent cloud platforms, AWS and Azure, specifically in the context of deploying machine learning models. Through both empirical and theoretical examination, we successfully tested and reported the results obtained from both platforms, shedding light on their respective capabilities and performance. This paper has provided insights into the strengths and limitations of AWS and Azure for machine learning applications. Our findings highlight crucial aspects such as the upload time, download time, inference time, throughput, cost effectiveness, variety in the catalog of ML resources, and ease of use, which are all pivotal considerations for practitioners and researchers in the field. Our results indicate that Azure exhibits a superior download time with an average of 0.49 s/MB, enabling efficient data transfer from local environments to the cloud. On the other hand, AWS demonstrates a better download time with an average of 1.84 s/MB, ensuring the seamless retrieval of data from the cloud to local environments. In addition, Azure's infrastructure, along with autoscaling, leads to a faster inference time (average 0.60 s/MB) and higher throughput (1145.78 MB/s), facilitating real-time decision making and efficient data-processing workflows. However, the cost structure of AWS proves to be more affordable with a total cost of USD 0.5364 per day, making it an attractive option for businesses aiming to optimize their cloud expenditures. In addition, AWS offers a remarkable advantage in terms of the availability of machine learning resources, along with being easy to deploy machine learning models. Its diversified portfolio of developer tools and machine learning models provides users with a broad spectrum of options to cater to their specific needs and requirements. Finally, AWS offers a more robust SLA, ensuring the minimal downtime and maximum connectivity of deployed applications. Nevertheless, with regard to the download and upload times in Azure, we speculated the presence of a dynamic priority allocation for read and write requests into Azure Blob Storage. This mechanism can significantly impact the data transfer efficiency and overall performance. To optimize the use of Azure Blob Storage for a wide range of applications, a deeper investigation is necessary to understand the mechanics of this allocation and its implications. By gaining a comprehensive understanding of this process, we can develop more-effective strategies and solutions to enhance the speed and reliability of data transfers, ultimately benefiting users across various use cases. Hence, we believe it is crucial to prioritize further research into the upload and download times within Azure Blob Storage. Additionally, subsequent research concerning this topic might incorporate various other object-detection models other than YOLOv8. This evaluation could validate whether the noted performance is specific to the model or uniform across different algorithms. Moreover, a long-term performance assessment could provide understanding of the stability and reliability of cloud services, crucial for business applications demanding high availability. Lastly, an investigation into the scalability procedures and the flexibility that cloud-service providers present in relation to resource allotment and administration could be undertaken. Such an inquiry would be essential for enterprises expecting rapid growth. In conclusion, these potential avenues for future research not only promise a more comprehensive understanding of model-specific performance but also hold the key to ensuring the sustained reliability and scalability of cloud services, offering valuable insights for businesses navigating the dynamic landscape of cloud computing. The comparative insights in this paper provide practitioners and researchers with a solid foundation for selecting the most suitable cloud platform for their machine learning projects.