Differences between Edge AI and Cloud AI
The edge AI is designed in a way that is monitors specific set of devices whereas the cloud AI can easily monitor more large amount of devices and data and provide better view for large environment more accurately, apart from all that here are some more key differences between the Edge AI and Cloud AI:
Feature |
Edge AI |
Cloud AI |
---|---|---|
Processing Location |
Edge AI supports on device processing (smartphones and IoT devices etc.) |
Cloud AI supports remote servers for processing (data centres etc.) |
Latency |
Edge AI has low latency because it processes the data locally. |
Cloud AI has more latency because it processes the data by transmitting it to the servers. |
Connectivity |
Edge AI can work even when it is offline. |
Cloud AI requires continues internet connectivity to work properly. |
Bandwidth |
Edge AI requires very low bandwidth requirements. |
Cloud AI has higher bandwidth requirements. |
Security |
Edge AI is more secure because the data stays within the device. |
Cloud AI is less secure because it transmits the data over the network. |
Cost |
Edge AI has lower cost initially but the maintenance cost is higher. |
Cloud AI has lower initial cost as well but here the cost is based upon the usage. |
Privacy |
Edge AI offers more privacy because of device limitations. |
There are some concerns about the data privacy in case of cloud AI. |
Custominzation |
Edge AI has limited adaptability because of limited device capabilities. |
The Cloud AI can adapt to the changing demands more quickly. |
Edge AI for Network Traffic Optimization
The edge AI for the network traffic algorithm refers to the various applications of the artificial intelligence and its algorithms and techniques at the edge of a network which are used in order to optimise and manage the network and its traffic more efficiently, So in this article we will understand what is edge AI, applications, advantages and disadvantages of using edge AI.
Edge AI means that we deploy AI models directly onto the network devices. for example, the routers switches or even the edge computing devices we deploy these in order to perform various tasks such as traffic prediction analysis as well as optimisation and this happens in the real time without having the need to send the data to a cloud or some centralised server.