Machine Learning Project RAID Log
This is a practical guide to building a raid log for a machine learning project project — a consolidated log of Risks, Assumptions, Issues and Dependencies, adapted to the realities of building and deploying a machine-learning model.
What a RAID Log is
A raid log is a consolidated log of Risks, Assumptions, Issues and Dependencies. For the full concept and how it works in general, see RAID Log. 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 raid log 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
- Risks
- Assumptions
- Issues
- Dependencies
- Owners and status
Machine Learning Project-specific considerations
Tailor the raid log 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 raid log 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.