Example Concept: Engineers can apply model pruning to remove less important parameters from a neural network, reducing its size and computational requirements while maintaining accuracy. Quantization techniques convert model weights to lower precision, decreasing memory usage and speeding up inference. Additionally, using efficient architectures like MobileNet or EfficientNet can provide a good trade-off between accuracy and computational cost.