Model Optimization

The process of improving the speed, latency, throughput, memory usage, or cost-efficiency of AI model inference through techniques such as quantization, pruning, batching, caching, hardware acceleration, and optimized model serving.

The process of further training a pretrained AI model on a task-specific or domain-specific dataset to improve its performance, behavior, or specialization for particular use cases.

The process of reducing the precision of a model's weights and activations to lower memory footprint and speed up inference.

A model optimization technique in which a smaller AI model, called the student model, is trained to mimic the behavior or outputs of a larger, more capable teacher model, reducing model size and inference cost while preserving much of the original performance.