Stage 1 · Lesson 8 of 8
Containerise the project
A packages your code, a Python runtime and the locked dependencies into one file system that runs the same way on a laptop, in CI and in the cloud. You describe it in a Dockerfile: start from a slim Python base image, copy pyproject.toml and uv.lock first, install dependencies, then copy the source.
Ordering it this way lets reuse the dependency layer when only code changes, which makes rebuilds fast. Add a .dockerignore so data, notebooks and secrets stay out. Both training jobs and ship as images, and and serving platforms expect them.
Common trapcopying the whole repository into the image, which bloats it and can bake a secret or a dataset into something you push to a registry.
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Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Python Fundamentals
Design Gurus · Beginner · $50
If your Python stops at notebooks: modules, functions, classes and error handling done properly
- CourseFreeMade With ML
Goku Mohandas · Advanced
Free: the scripting, CLI, testing and documentation lessons turn notebook code into a real project