You have now built a simple web service capable of serving predictions from your trained model. The next challenge is ensuring this service runs consistently, regardless of where it's deployed – be it your local machine, a teammate's computer, or a production server. Differences in operating systems, installed libraries, and configurations often lead to the common problem: "It works on my machine!"

This chapter introduces containerization, a method for packaging your application along with all its dependencies, libraries, and configuration files into a standardized unit. We will focus on Docker, a widely used platform for building and running these containers.

You will learn:

• The fundamental concepts behind containerization.
• The basics of Docker, including images and containers.
• How to write a Dockerfile to define the environment for your application.
• Steps to build a Docker image for the Flask prediction service developed previously.
• How to run your application inside a Docker container.

By the end of this chapter, you will be able to package your simple machine learning prediction service into a portable Docker container.