#Model Compression

Model compression is the art of trimming down bloated machine learning models, menacingly balancing human patience and cloud bills with a wry grin. It elevates runtime efficiency over theoretical elegance, absolving developers of guilt while slashing operational costs in one fell swoop. Beyond every size reduction lurks the ghost of inference errors, forever haunting the diligent. It offers an alchemy of anxiety and productivity to those who taste the forbidden fruits of pruning and quantization under harsh constraints. Ultimately, model compression stands as a jester forcing performance and accuracy to tango through a labyrinth of lightness.