Geek Out Time: Exploration of Model Pruning for Efficient Deployment

This week, I’m exploring model pruning — a technique that removes unimportant weights from a neural network to make it more efficient. Pruning is commonly used to reduce model size and, in some cases, improve inference speed, which is ideal for deploying models in resource-constrained environments. In this blog, I’ll share a two-step iterative process of pruning a ResNet50 model, where each step reveals new insights into pruning’s impact on model size and performance.

This is an excerpt — the full article continues on Medium.