A factory that sits in a rural area must deploy a vibration-analysis model that can shut down heavy machinery within 10 ms when an anomaly is detected. The only WAN link is a low-bandwidth satellite connection that is often unavailable for several hours. Sending the raw sensor stream to a cloud or off-site data center would exceed both the latency budget and the link's capacity. Which deployment environment best satisfies the project's technical constraints?
Edge deployment on embedded gateways mounted to each machine
Containerized microservices running in a public-cloud region with GPU autoscaling
On-premises cluster located at the organization's main data center 500 km away
Hybrid deployment with real-time inference in the cloud and periodic data replication at the plant
Running the model on an edge device places compute directly on-or very near-the industrial IoT gateways that collect the vibration data. Local inference eliminates the WAN round-trip so the system can react in well under 10 ms, and it continues to operate when the satellite link is down. Public-cloud or off-site data-center deployments cannot meet the latency target and would fail during connectivity outages, while a hybrid design that keeps inference in the cloud still relies on the same unreliable link. Therefore, the edge deployment is the only option that meets both the latency and connectivity requirements.
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