After training a machine learning model, the immediate task is to save its learned state. Without saving, the model exists only in memory and is lost once the training script finishes. This chapter addresses how to make your models persistent and ready for use in different environments or at a later time.

You will learn the concept of model serialization, the process of converting your trained model object into a format that can be stored (like a file) and then loaded back into memory later. We will cover two common Python libraries for this purpose: pickle and joblib, highlighting their usage and when one might be preferred over the other.

Furthermore, preparing a model involves more than just saving the algorithm's weights. We will discuss the necessity of tracking and managing the software libraries (and their versions) your model depends on. You will also learn how to save the data preprocessing steps (like scalers or encoders) that must be applied consistently to new input data before making predictions. The chapter includes practical examples to guide you through saving and loading a simple model.