What is meant by Edge AI Computing?
- Edge AI Computing in IT is defined as the deployment of data-handling activities or other network operations away from centralized and always-connected network segments, and toward individual sources of data capture, such as endpoints like laptops, tablets or smartphones.
- Edge AI Computing deployments are ideally suited in a number of situations. One is when IoT devices have insufficient connectivity and it is not feasible for IoT devices to be seamlessly connected to a central cloud. High latency, low spectral efficiency, and non-adaptive machine type of communication are some of the serious challenges of cloud computing framework that is leading to a shift to computing to the edge devices of the network.
- Basic data visualization
- Basic data analytics and short term data historian features
- Data caching, buffering and streaming
- Data pre-processing, cleansing, filtering and optimization
- Some data aggregation
- Device to Device communications/M2M
What is the difference between Cloud and Edge AI Computing ?
- Edge AI computing refers to data processing power at the edge of a network instead of holding that processing power in a cloud or a central data warehouse. Edge computing does not replace cloud computing, however. In reality, an analytic model or rules might be created in a cloud then pushed out to edge devices.
- Both the systems of computing are useful; however, the usage of a particular technology is determined by the area of application – as in the case of these two. One does not override the other; neither can they dominate each other. Cloud computing functions as a normal server and every process that happens are accounted for within the server. Data management, critical mission response, processing data, etc. all happens within the cloud storage.
Feel free to contact E-SPIN for Edge AI and related project or operation requirement, from consultancy, end to end technology solution, from infrastructure monitoring, security testing and continuous protection.