8. Model Deployment

A model that lives only in a notebook creates no value. Deployment is the act of moving a trained model out of the experimental environment and into a system where it can receive inputs, produce predictions, and be relied upon by other people and processes.

This chapter covers the foundational concerns of getting a model into production. We start with serialization—how to save a trained model to disk and reload it later without retraining. We then address containerization—how to package a model together with all its dependencies so it runs identically on any machine. Finally, we survey the broader deployment landscape, examining where and how models are typically served in industry.

These three topics build on each other. A model must be serialized before it can be shipped. It must be containerized before it can be deployed reliably. And understanding the deployment landscape helps you choose the right strategy for your scale, latency requirements, and team.