AI is Now Everywhere
 
As businesses across industries grapple with vast amounts of data, more complex operations, and more dynamic markets, edge AI is playing a growing role in helping them rapidly respond. Through a combination of computing power, AI technology, data analytics, and advanced connectivity, the edge extends compute capabilities from data centers out to the edge of networks, allowing organizations to act quickly on data where it’s captured. Reducing the distance between where data is captured and where it’s processed not only alleviates data transit costs, but also improves latency, bandwidth utilization, and infrastructure costs.
 

Addressing the Requirements of Edge AI
 
AI at the edge comes with a unique set of requirements. Edge systems, dispersed across vast physical distances, lack the centrality that a data center presents. Software or system updates either need to be performed manually or to be centrally managed to easily deploy, manage, and scale software across vast fleets of devices. Moreover, the security requirements for edge computing infrastructure differ from cloud or data center computing models. Edge locations lack the physical security that data centers have, so an end-to-end security model that protects both the application intellectual property and the sensor data is paramount for a successful deployment.