# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 2 Literature Review
### Deep Learning on the Cloud  
Cloud computing has had a profound impact on deep learning. It has revolutionized the way deep learning models are developed, deployed, and utilized. The main impacts include the improvements in scalability, cost efficiency, and high availability of resources. These impacts translate to the various metrics that need to be measured when comparing between cloud providers since they highlight the important factors that are considered when choosing a cloud provider for the process of model deployment.  
The application of cloud computing in deep learning arises mainly from the requirement of quick real-time inference. For instance, using cloud infrastructure for deep learning is vital in domains such as security and medicine, where traits such as scalability, redundancy, and availability are of paramount importance, as systems in such domains generally deal with sensitive data and require low to no downtime for the optimal functioning of the systems [9; 10; 11]. Therefore, the requirement of the high availability of deep learning model resources implies that metrics such as the latency, inference time, and throughput time of such deep learning systems are of prime importance when measuring the performance of the cloud ecosystems that these models are deployed in. Overall, the inherent necessity of low downtime for security and medical applications points to a need to quantitatively measure the various temporal metrics of a deep learning system in the cloud.  
The availability and monetary cost of machine learning and deep learning resources in the cloud is another important factor when measuring performance. Papers such as [12] illustrate the importance of cost and availability, particularly in the context of Internet-of-Things (IoT) devices. IoT systems have grown in popularity over the last few years because of their possible low cost and remote deployment capabilities. Hence, the lower cost and high availability of ML services via the cloud allows IoT systems to improve their use cases.  
Cloud computing has also had an immense impact on Generative AI systems. It has been a key enabling factor in the development of massive Generative AI models such as LLMs (Large Language Models) [13; 14] and Diffusion Models [15]. FalconLLM [13] is the one of the latest renditions in the line of LLMs and is currently one of the best-performing open-source language models. This model was trained on a cluster of 384 GPUs on AWS over a period of two months. Due to the fast-paced nature of the research and development in the field of Generative AI and LLMs, the ease of deployment of deep learning models on cloud platforms becomes a relevant discussion. Therefore, the choice between cloud providers can also be informed by their usability and richness in features to aid the deployment process of deep learning models. Overall, cloud computing addresses the practical needs of deep learning, and therefore the need arises to quantitatively and qualitatively measure the various metrics pertaining to the deployment cycle of a deep learning model in the cloud.