Its crucial that the time and memory cost of running inference in a ML model is lower than the time and memory cost of training. Model compression is a strategy for reducing the cost of inference. Its main idea is to replace the original, expensive model with a smaller model that requires less memory and runtime to store and evaluate. This strategy can be used when size of the original model needs to prevent overfitting.