Recurrent Neural Network (RNN) Inference Cloud Computing

Abstract

The purpose of this technical article is to investigate the convergence of Recurrent Neural Networks (RNNs) with Cloud Computing considering the potential benefits and challenges that may be encountered in bringing these two systems together. Recurrent Neural Networks (RNNs) constitute a particular type of artificial neural network that is capable of processing data sequentially in such a way that it becomes well-suited for applications that involve handling time series or language processing. On the other hand, cloud computing provides computer infrastructures on a scalable basis as well as flexible computation services that are accessible whenever they are needed. This paper targets making RNN applications more efficient and faster with the use of RNN libraries on cloud computing platforms through which the cloud services can be used for things like data processing power and storage. By combining RNNs with Cloud Computing, this paper aims to enhance the efficiency and performance of RNN-based applications by leveraging the cloud's computational power and storage capabilities. The study investigates the impact of deploying RNN inference tasks in the cloud environment, analyzing factors such as latency, cost-effectiveness, and scalability.