Inference optimization involves improving the performance and speed of machine learning models when they are making predictions on new data. This can include techniques like model pruning, quantization, and utilizing more efficient hardware or software. It is crucial in real-world applications where quick and efficient responses are required, such as in autonomous vehicles or real-time recommendation systems. By optimizing inference, systems can achieve faster decision-making with lower computational resources.