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AI at the edge means we’re simply moving at least portions of the process out of centralized data centers closer to where the data originates and where decisions are made in the physical world. This trend is being driven by the exponential growth of devices and data and reasons include reducing latency and network bandwidth consumption and ensuring autonomy, security and privacy. Many of the general considerations for deploying AI in the cloud carry over to the edge. For instance, results must be validated in the real world — just because a particular model works in a pilot environment doesn’t guarantee that the success will be replicated when it’s deployed in practice.
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