AI Infrastructure
From Cloud to Edge: Deploying AI Where Data Lives
Not every AI workload belongs in a centralized cloud. Latency-sensitive applications, bandwidth constraints, and data residency rules often push inference closer to devices and local servers.
Edge AI shines for cameras, sensors, and shop-floor systems. Cloud AI remains strong for training, heavy batch jobs, and collaborative analytics.
Hybrid architectures are increasingly common: train in the cloud, deploy compact models at the edge, and sync insights securely.
Choosing the right placement early avoids expensive redesigns and keeps AI experiences responsive for users.
