Machine Learning Project Quality Plan
This is a practical guide to building a quality plan for a machine learning project project — how quality will be planned, assured and controlled, adapted to the realities of building and deploying a machine-learning model.
What a Quality Plan is
A quality plan is how quality will be planned, assured and controlled. For the full concept and how it works in general, see Quality Management 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 quality 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
- Quality standards
- Quality metrics
- QA activities
- QC / inspection approach
Machine Learning Project-specific considerations
Tailor the quality 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 quality 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.