After investing time and computational resources to train a machine learning model, you'll want to save its learned state. Saving a model allows you to stop and resume training later, use it for making predictions on new data without retraining, or share it with others. This chapter focuses on the practical aspects of model persistence in TensorFlow and Keras.

You will learn how to:

• Save model weights periodically during training using checkpoints.
• Save only the model's learned parameters (weights).
• Save the complete model, including its architecture, weights, and optimizer configuration.
• Understand the standard TensorFlow SavedModel format, suitable for deployment.
• Load previously saved weights or entire models for inference or continued training.
• Get introduced to TensorFlow Hub for accessing pre-trained model components.

We will cover the different methods available and discuss when to use each approach, ensuring you can effectively manage your trained models.