.. _ch:deploy:

Model Deployment
================

In earlier chapters, we discussed the basic components of the machine
learning model training system. In this chapter, we look at the basics
of model deployment, a process whereby a trained model is deployed in a
runtime environment for inference. We explore the conversion from a
training model into an inference model, model compression methods that
adapt to hardware restrictions, model inference and performance
optimization, and model security protection.

The key aspects this chapter explores are as follows:

1. Conversion and optimization from a training model to an inference
   model.

2. Common methods for model compression: quantization, sparsification,
   and knowledge distillation.

3. Model inference process and common methods for performance
   optimization.

4. Common methods for model security protection.

.. toctree::
   :maxdepth: 2

   Overview
   Conversion_to_Inference_Model_and_Model_Optimization
   Model_Compression
   Advanced_Efficient_Techniques
   Model_Inference
   Security_Protection_of_Models
   Chapter_Summary
   Further_Reading