Model optimization encompasses techniques that improve a trained model's efficiency — reducing latency, memory usage, and computational cost — without significantly degrading prediction quality. Methods include quantization, pruning, knowledge distillation, operator fusion, and graph optimization. Optimization is critical for deploying models at scale: a model that takes 2 seconds per prediction may be too slow for a real-time customer service bot, but optimized to 50 milliseconds it becomes production-ready.