Master the Art of Quantization: A Practical Guide

Exploring and Implementing Quantization Methods with TensorFlow and PyTorch

Master the Art of Quantization: A Practical Guide

Are you interested in deploying machine learning models on resource-constrained devices such as mobile phones or embedded devices?

If so, you may be familiar with the quantization strategy, which enables you to minimize the size of your model while speeding it up for inference at the expense of some accuracy.

I’ll discuss the two primary methods of quantization in this blog: post-training quantization and quantization-aware training.

Additionally, TensorFlow and PyTorch code examples for using these techniques will be made available.

Whether you are new to quantization or have some experience with it, this post will provide you with the information and tools you need to deploy efficient and accurate machine learning models on resource-constrained devices.

Table of Contents

• Introduction to Quantization
• Post-Training Quantization
• Quantization-Aware Training
• Other Approaches to Quantization
• Best Practices
• Key Takeaways, Final Notes, and References