Published on the Microsoft Global Black Belts YouTube channel.
MLFlow is one of the most useful tools in the ML practitioner’s toolkit for tracking experiments, but getting it running cleanly in a Codespaces environment has some gotchas. This demo walks through a working setup - reproducible, shareable, and ready to use without fighting local environment drift.
The broader point: if you want ML work to be reviewable and reproducible, the dev environment needs to be treated as a first-class artifact.