Once you have successfully trained a model, the next logical steps involve preserving your work and preparing it for use in applications. This chapter focuses on these essential post-training operations within the PyTorch framework, drawing parallels to practices you might know from TensorFlow.

You will learn to:

• Manage model persistence using PyTorch's state_dict, contrasting this with TensorFlow's SavedModel and HDF5 formats.
• Distinguish between saving entire models versus only their parameters ($weights$ and $biases$) and understand checkpointing strategies.
• Prepare models for deployment using TorchScript, which facilitates serialization and optimization of your PyTorch models.
• Gain an understanding of ONNX (Open Neural Network Exchange) for model interoperability between different frameworks.
• Receive a brief introduction to TorchServe for serving PyTorch models.

By completing this chapter, you will be equipped to effectively save, load, inspect, and prepare your PyTorch models for various deployment scenarios, adapting your existing TensorFlow knowledge to the PyTorch ecosystem.