AI At The Edge Is Different From AI In The Datacenter
The Next Platform, Thursday, May 9th, 2024
Today's pace of business requires companies to find faster ways to serve customers, gather actionable insights, increase operational efficiency, and reduce costs. Edge-to-cloud solutions running AI workloads at the edge help address this demand.
Placing compute power at the network edge, close to the data creation point, makes a vital difference for near-real-time use cases. Handling functions like computing algorithms and data processing on local devices rather than moving those workloads to the cloud data center dramatically reduces latency.
In manufacturing, locally run AI models can rapidly interpret data from sensors and cameras to perform vital tasks. For example, automakers scan their assembly lines using computer vision to identify potential defects in a vehicle before it leaves the plant. In a use case like this, very low latency and always-on requirements make data movement throughout an extensive network impractical. Even small amounts of lag can impede quality assurance processes.