Practice of Applied Edge Analytics in Intelligent Learning Framework

The advent of IoT has brought in a huge amount of data that is exponentially growing every second. This seemingly growing data has paved the way to rethink on various technologies to capture the data and analyze it properly. Such huge data is the fuel for various analytics and intelligent systems like machine learning and deep learning applications. The deployment of machine learning and deep learning intelligence across the analytical network takes place in the central data system (cloud servers) which is a very expensive challenge in terms of time, money, data privacy. But the application of such intelligence at the edge computing, which is a new paradigm of the cloud-enabled network, has solved the problem by offering high security and reliability. Unlike Cloud computing, edge computing is a decentralized, distributed architecture where analytics and insight happens near or at the data source itself that solves the expensive challenges mentioned above. This paper describes the network of edge computing and its variance from cloud computing, edge architecture, and diverse applications of machine learning algorithms and deep learning framework deployed at the edge network for intelligent analytics.