How do you scale ML inference systems to handle high traffic? What are the considerations for horizontal vs vertical scaling, and how do you handle traffic spikes?

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Scaling ML inference systems presents unique challenges due to the computational intensity of model predictions and the state management required for efficient operation. The right scaling approach depends on model characteristics, traffic patterns, and latency requirements.