How do you optimize ML models for low-latency inference in real-time applications?

Instruction: Discuss techniques and considerations for optimizing ML models to achieve low latency in real-time inference scenarios.

Context: This question evaluates the candidate's ability to refine ML models and systems for high-performance real-time applications, where latency is a critical factor.

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The way I'd approach it in an interview is this: I optimize for low-latency inference by profiling the full request path, then simplifying the parts that dominate tail latency. That can include model compression, feature simplification, caching, batching changes, faster runtimes,...

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