Scott Andery

This is a useful overview of AI model optimization, especially because it connects techniques like quantization, pruning, distillation, caching, and batching to practical goals such as faster inference and lower compute costs. I also liked the clear comparison of how each technique can affect speed, memory, and model quality. The real-world examples make the topic easier to understand for teams looking to optimize AI workloads.