Machine Learning Project Assumptions Log
This is a practical guide to building a assumptions log for a machine learning project project — a register of the assumptions the plan depends on, to be validated, adapted to the realities of building and deploying a machine-learning model.
What a Assumptions Log is
A assumptions log is a register of the assumptions the plan depends on, to be validated. For the full concept and how it works in general, see Project Assumption. 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 assumptions 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
- Assumption
- Impact if wrong
- Owner
- Validation status
Machine Learning Project-specific considerations
Tailor the assumptions 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 assumptions 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.