Docker Basics for ML

Summary

Packaging your model API and its dependencies into a portable container that runs identically anywhere — solves the classic 'it works on my machine' problem for ML deployments specifically.

• 1Write a Dockerfile that packages your model API from the previous topic.
• 2Build and run the container locally, confirming it behaves the same as your local setup.
• 3Learn why pinning exact library versions matters even more for ML than typical software.