Example Concept: Developers can balance model accuracy and performance by employing techniques such as hyperparameter tuning, model pruning, and quantization. Hyperparameter tuning involves adjusting parameters like learning rate and batch size to optimize model performance. Model pruning reduces the size of the model by removing less important weights, which can improve inference speed without significantly affecting accuracy. Quantization reduces the precision of the model weights, leading to faster computation and lower memory usage, often with a minimal impact on accuracy.