  With time, machine learning models have increased in their scope,
functionality and size. Consequently, the increased functionality and size of
such models requires high-end hardware to both train and provide inference
after the fact. This paper aims to explore the possibilities within the domain
of model compression, discuss the efficiency of combining various levels of
pruning and quantization, while proposing a quality measurement metric to
objectively decide which combination is best in terms of minimizing the
accuracy delta and maximizing the size reduction factor.
