Machine Learning Project Requirements Document
This is a practical guide to building a requirements document for a machine learning project project — the documented needs the solution must meet, adapted to the realities of building and deploying a machine-learning model.
What a Requirements Document is
A requirements document is the documented needs the solution must meet. For the full concept and how it works in general, see Requirements Document. 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 requirements document 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
- Business requirements
- Functional requirements
- Non-functional requirements
- Acceptance criteria
- Traceability
Machine Learning Project-specific considerations
Tailor the requirements document 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 requirements document 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.