Most people recognize that AI can’t happen without data centers. But not everyone knows that what we call “AI” isn’t just one process happening in one place. It’s a series of interrelated workloads distributed across different types of data centers in different locations, and there’s often nuance required when it comes to determining which workloads should go where. These AI workloads can be divided into three main categories: AI model training: How organizations develop their models. This involves processing massive volumes of data in order to establish pattern recognition. AI inference: How organizations apply their models. This involves making predictions based on…

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