Limitations—such as latency, bandwidth costs, privacy concerns, catastrophic consequences in the event of failure, and dependency on continuous connectivity—are driving interest in Edge AI.
Over the past decade, cloud-based artificial intelligence (AI) has undergone significant maturation. Cloud-based AI now reliably supports large-scale model training, massive data storage, and centralized orchestration of AI workloads. At the same time, limitations—such as latency, bandwidth costs, privacy concerns, catastrophic consequences in the event of failure, and dependency on continuous connectivity—are driving interest in Edge AI—processing, inference, and sometimes even training—occurring much closer to where data is generated.
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