Example Concept: Model pruning involves removing less important parameters from the neural network, reducing its size and computational load without significantly affecting output quality. Quantization converts model weights to lower precision, which speeds up processing. Additionally, using optimized architectures like EfficientNet or leveraging hardware accelerators such as GPUs and TPUs can further enhance speed while preserving image quality.