Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Cloud Computing › Snowflake › AI/ML › Model Deployment

Latency problems

concept · part of Model Deployment

Latency refers to the delay in processing a prediction request, which can significantly impact user experience in real-time applications such as recommendation engines, fraud detection, or chatbots. Causes include large model size, inefficient code, insufficient compute power, or network issues. Mitigation techniques include quantization (reducing precision of model weights), model pruning (removing unnecessary parameters), using more efficient architectures, and deploying on hardware with sufficient resources (e.g., GPUs, edge devices). For example, a financial institution deploying a fraud detection model can optimize using ONNX and deploy on GPU-based servers to reduce latency.

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