Model Deployment & Serving

Getting trained models into production where they serve real users. Involves containerization (Docker), API frameworks (FastAPI), model servers (TensorRT, vLLM), and monitoring. Serving splits two ways: real-time endpoints optimized for latency, and batch jobs optimized for throughput and cost.

Key terms

DockerFastAPITensorRTvLLMLatencyThroughputTriton & inference serversBatch vs real-time

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