How does model compression pipeline design influence bias and variance in edge ML systems?

Model compression reduces variance and latency but often increases bias due to loss of representational capacity.

Model compression techniques like pruning, quantization, and knowledge distillation are widely used in edge ML systems to deploy lightweight models.

Compression reduces variance because simpler models are less sensitive to noise and more stable. However, it increases bias because reduced parameters limit expressive power.

Distillation helps mitigate this tradeoff by transferring knowledge from a large teacher model to a smaller student model. Quantization reduces precision, which can introduce approximation errors but significantly improves inference efficiency.