Machine Learning Project Project Charter
This is a practical guide to building a project charter for a machine learning project project — the short authorising document that names the sponsor, states the objective and gives the manager authority to run the project, adapted to the realities of building and deploying a machine-learning model.
What a Project Charter is
A project charter is the short authorising document that names the sponsor, states the objective and gives the manager authority to run the project. For the full concept and how it works in general, see Project Charter. 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 project charter 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
- Purpose and business justification
- Objectives and success criteria
- High-level scope (in and out)
- Budget and timeline summary
- Key stakeholders and sponsor sign-off
Machine Learning Project-specific considerations
Tailor the project charter 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 project charter 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.