How do TensorFlow developers optimize model performance and scalability?
The question is about Tensorflow
TensorFlow developers optimize model performance using different scalable techniques such as quantization, pruning, and distributed training. Quantization, by reducing the model size because of lower-precision parameters, increases speed during inference on edge devices. Pruning gets rid of unimportant weights in a model and leads to leaner models with very limited loss in accuracy. Distributed training in TensorFlow enables models to train across multiple GPUs or TPUs all at once, greatly increasing the processing speed for large volumes of data. While performance is tracked using TensorBoard, serving with TensorFlow Serving allows him to easily deploy and scale his model in production for efficiently handling high-volume requests.
