Machine Learning Project Decision Log
This is a practical guide to building a decision log for a machine learning project project — a record of the key decisions made, by whom and why, adapted to the realities of building and deploying a machine-learning model.
What a Decision Log is
A decision log is a record of the key decisions made, by whom and why. For the full concept and how it works in general, see Decision 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 decision 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
- Decision
- Date and owner
- Rationale
- Alternatives considered
- Impact
Machine Learning Project-specific considerations
Tailor the decision 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 decision 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.