Building a machine learning model that performs well on historical data is a significant achievement, but its value is often realized only when it can be used to make predictions on new, unseen data. This chapter focuses on the essential steps required to take a trained model and make it accessible and operational.

You will learn the fundamental practices for deploying models, starting with methods for saving and loading trained models to ensure persistence. We will then introduce the concept of model serving and guide you through building a simple REST API using common Python web frameworks (like Flask or FastAPI) to expose your model's prediction capabilities. Finally, we'll cover containerization using Docker as a way to package your model and its dependencies for consistent deployment, along with introducing basic concepts for monitoring models once they are in production. This chapter provides the foundational knowledge needed to bridge the gap between model development and practical application.