Edge AI: Pushing Workload Boundaries

The larger and more complex the data set involved, the harder applications have to work to ingest, process and analyse the information. That puts considerable strain on the underlying network, storage and server architecture which shifts data from one place to another for analysis, a situation that often creates latency and bandwidth bottlenecks that can undermine application performance and compromise security and regulatory compliance policies.

This report discusses how and where edge compute architectures and approaches can help to solve those performance issues by processing data in distributed locations rather than centralised data centres, particularly when it comes to the use of artificial intelligence (AI) technology.

Ways to access this research