How can I optimize the performance of my AI library?

How can I optimize the performance of my AI library?

To optimize your AI library's performance, consider techniques like model pruning, quantization, and using efficient data structures.

What is model pruning and how does it help?

Model pruning involves removing weights from a neural network that contribute little to its output, reducing size and improving speed.

Are there specific libraries or tools for quantization?

Yes, libraries like TensorFlow Lite and PyTorch provide tools for quantization to help reduce model size and improve inference speed.

How can I measure the performance improvements after optimization?

You can measure performance improvements using metrics like inference time, memory usage, and accuracy on a validation dataset.