Containerizing a Machine Learning App With Flask and Docker

Added Jun 2025 3 design docs

The models that matter are the ones other people can run, and containerization is how modern teams guarantee that. This project is a focused first encounter with Docker in a machine learning context: take a working model, wrap it in a web service, and make it run identically on any machine. The intern trains a simple scikit-learn model in Python and serves it through a small Flask application with a prediction endpoint. They then write a Dockerfile from scratch — choosing a base image, installing dependencies, copying application code, and defining the startup command — and build and run the container locally, verifying that the service responds correctly from inside its isolated environment. Clear written instructions for building and running the image round out the project, because reproducibility includes the documentation, not just the artifact. Though deliberately small, the project delivers one of the most transferable skills in the field. The intern finishes understanding images versus containers, how dependencies get frozen into a build, and why containerized services behave consistently across machines — knowledge they will reuse in nearly every deployment task of their career.

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