Model cards and ML governance
Document models honestly and set up light, real governance that helps people make good decisions
A taste of a lesson
Our card says 'Not suitable for high risk use.' Is that a good out of scope statement?
It is a start, but too vague to help anyone decide. Readers may disagree about what 'high risk' means, and nobody can check against it. Name concrete uses instead: for example, 'Not for screening job applicants', 'Not for decisions about individual customers' credit', 'Not validated for languages other than English or Spanish'. Tie each to a reason, such as missing evaluation or known weaknesses. Then a product manager can tell immediately whether their plan fits. Your turn: write two specific out of scope uses for your model and the reason behind each.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Write a complete, honest model card for a real or example model
- Write a short datasheet describing a dataset's origin and limits
- Assign risk tiers and proportionate approval steps to model uses
- Keep audit trails and a review schedule so documents stay accurate
- Frame regulatory questions to take to legal and compliance colleagues
Lesson plan
- 1 Who documentation is for Identify the readers of a model card and the decisions they need to make. Start
- 2 Writing the model card Draft each section of a model card for your running example. Start
- 3 Evaluation by subgroup and condition Report results across groups and conditions so hidden weaknesses are visible. Start
- 4 Datasheets for datasets Document a dataset's purpose, collection, consent and known issues. Start
- 5 Risk tiers and approvals Design proportionate review steps based on the impact of each use. Start
- 6 Keeping it current and asking the right questions Maintain documents over time and prepare questions for legal and compliance. Start
Try asking
About this tutor
For engineers, product owners and team leads who need to document models and decide who approves what before a model affects people. You will write a model card section by section, from intended use and out of scope uses to subgroup evaluation and known limitations, and a short datasheet for the dataset behind it. Then you design governance that fits your organisation: risk tiers, approval steps proportionate to impact, audit trails and a schedule for keeping documents current. Regulation is discussed in broad terms only, so you know which questions to ask your legal and compliance colleagues. Every lesson uses a running example model that you document as you go.
Reviews
4.0
3 ratingsSample
- Hyun-woo P.Sample
Rewriting our vague out of scope section into concrete uses with reasons changed a product discussion the same week. Very practical.
- Beatriz S.Sample
Good balance between documentation and governance. The tutor was clear it would not give legal advice, which annoyed me briefly but was right.
- Owen T.Sample
Useful templates and reasoning. For my small team the risk tier lesson felt a bit heavy, though the principles still applied.
About the teacher
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9 tutors 322 lessons taught Sample
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I...
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