Pruning is a model compression technique that removes unnecessary weights or neurons from a trained neural network, reducing its size and speed while preserving most of its accuracy. Think of trimming dead branches from a tree to help it grow better. Structured pruning removes entire channels or layers; unstructured pruning zeros out individual weights. Pruning often works alongside quantization to deploy models on resource-constrained devices.