Model Cards
Project phase: Machine Learning Model Engineering
You document each model you deliver in a standard, shareable card so someone who didn't build it can judge whether to use it: intended use and out-of-scope uses, training and evaluation data with their known limitations, architecture and key hyperparameters, and evaluation metrics reported overall and broken down by relevant subgroups. You add the quality considerations that matter — fairness, robustness, behaviour on unfamiliar data — with caveats and recommendations for responsible use, versioned alongside the model artefact it describes.
Starting Points
Key Points
- The Model Card is a standalone Markdown document, stored in the same GitLab repository as the model.
- The documentation uses industry standards (like Google's Model Card framework) to describe the model.
- I have validated the model's performance by reporting results for specific subgroups, not just a single global score.
- I have explicitly addressed end-user impact, such as privacy, security, or potential bias in the results.
- The language is precise and technical, but clear enough for a developer outside my immediate team to understand.
- The card is version-matched to the model artefact (the documentation matches the actual code/weights).