Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide
range of important real-world applications. DNNs consist of a huge number of parameters that require
millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A
more effective method is to implement DNNs in a cloud computing system equipped with centralized
servers and data storage sub-systems with high-speed and high-performance computing capabilities.
This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
Various DNN complexities associated with different architectures are presented and discussed alongside
the necessities of using cloud computing. We also present an extensive overview of different cloud
computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications
already deployed in cloud computing systems are reviewed to demonstrate the advantages of using
cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing
systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The
Consolidated Research Group MATHMODE (IT1456-22
