How would you approach optimizing a machine learning model for a low-latency application?

Instruction: Discuss considerations for model selection, training, and deployment to achieve low latency.

Context: This question assesses the candidate's ability to balance model complexity and performance, particularly in applications requiring quick responses.

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I would begin with a hard latency budget and profile the system end to end before I changed the model. In low-latency systems, the bottleneck is often not the model itself. It can be feature fetching, serialization, network hops, or an overly expensive fallback path.

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How would you approach optimizing a machine learning model for a low-latency application?

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