Machine Learning Project Scope Statement
This is a practical guide to building a scope statement for a machine learning project project — a definition of deliverables, boundaries and acceptance criteria, adapted to the realities of building and deploying a machine-learning model.
What a Scope Statement is
A scope statement is a definition of deliverables, boundaries and acceptance criteria. For the full concept and how it works in general, see Scope Statement. 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 scope statement 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
- Deliverables
- In scope
- Explicitly out of scope
- Acceptance criteria
- Constraints and assumptions
Machine Learning Project-specific considerations
Tailor the scope statement 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 scope statement 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.