"It worked on my machine" is the oldest problem in software, and in machine learning it is worse: models depend on specific Python versions, libraries, and system packages. Containers solve this by bundling your code and everything it needs into a single image that runs the same way on a laptop, a CI server, or a cloud cluster.
You'll start with Docker and why it matters for machine learning, including how to turn a trained Python model into a Docker image. Then you'll see why running many containers by hand doesn't scale, and how Kubernetes orchestrates them across a cluster, handling scheduling, scaling, and recovery. The module finishes with Helm, the package manager for Kubernetes, which turns piles of YAML files into reusable, configurable releases.
With your models packaged and a place to run them, you're ready to automate how changes get built and shipped in the next module, Testing & CI/CD for ML.
How and why to use Docker for machine learning development, and what it does for reproducible environments.
A tutorial on packaging a machine learning model written in Python into a Docker image.
A beginner-friendly guide to what container orchestration is and how Docker and Kubernetes fit together.
What Helm is and why it is known as the package manager for Kubernetes, and how it helps manage applications beyond raw YAML files.