Having trained a model, the next step involves preparing it to make predictions on new data. This chapter focuses on packaging your trained machine learning models and the necessary inference code into Docker containers. The goal is to create self contained, efficient units ready for serving predictions.

You will learn practical techniques for:

• Designing and building simple inference services, often using web frameworks like Flask or FastAPI, within a container.
• Optimizing the resulting Docker images for size and efficiency using methods such as multi stage builds and careful dependency management.
• Configuring containers for inference, including exposing network ports and implementing health checks to ensure the service is operational.
• Packaging models and associated artifacts effectively within the container environment.

By the end of this chapter, you will be able to construct optimized Docker containers that serve your trained ML models.