A trained machine learning model generates value only when applications can use it to make predictions. This chapter covers the technical processes required to move a model from a local environment into a production system where it can serve those predictions. We will examine the complete path from a saved model file to an operational, monitored service.

You will learn to:

• Package a model and its dependencies into a self-contained, portable environment using Docker.
• Expose a model's prediction function through a web API using a framework like Flask.
• Distinguish between online (real-time) and batch (offline) deployment patterns.
• Understand the role of a model registry in managing and versioning production-ready models.
• Grasp the fundamentals of monitoring a deployed model for operational and performance issues.

The chapter concludes with a hands-on section where you will containerize a simple model and prepare it for deployment.