Machine Learning Project Test Plan
This is a practical guide to building a test plan for a machine learning project project — the approach, scope and schedule for testing the deliverables, adapted to the realities of building and deploying a machine-learning model.
What a Test Plan is
A test plan is the approach, scope and schedule for testing the deliverables. For the full concept and how it works in general, see Test Plan. 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 test plan 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
- Test scope and objectives
- Test cases/scenarios
- Environments
- Entry/exit criteria
- Defect management
Machine Learning Project-specific considerations
Tailor the test plan 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 test plan 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.