Machine Learning Project Risk Register
This is a practical guide to building a risk register for a machine learning project project — the living log of risks with scores, owners and responses, adapted to the realities of building and deploying a machine-learning model.
What a Risk Register is
A risk register is the living log of risks with scores, owners and responses. For the full concept and how it works in general, see Risk Register. On a machine learning project project it plays the same role, tuned to this kind of work.
Why it matters for a Machine Learning Project project
Machine Learning Project projects live or die on building and deploying a machine-learning model. A well-built risk register gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how machine learning project projects drift into avoidable delay and cost.
What to include
- Risk description and category
- Probability and impact
- Risk score
- Owner
- Response and trigger
Machine Learning Project-specific considerations
Tailor the risk register to the risks that most often derail machine learning project projects:
- Data labelling and quality
- Model drift
- Productionisation and monitoring
Example
On a real machine learning project project, the risk register would be shaped by building and deploying a machine-learning model. In particular, it should explicitly account for the project’s biggest risks — data labelling and quality, model drift, productionisation and monitoring — rather than treating them as afterthoughts.