Machine Learning Project Stakeholder Register
This is a practical guide to building a stakeholder register for a machine learning project project — a record of stakeholders, their influence and how to engage them, adapted to the realities of building and deploying a machine-learning model.
What a Stakeholder Register is
A stakeholder register is a record of stakeholders, their influence and how to engage them. For the full concept and how it works in general, see Stakeholder Register. 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 stakeholder register 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
- Stakeholder name and role
- Power and interest
- Attitude
- Engagement approach
Machine Learning Project-specific considerations
Tailor the stakeholder register 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 stakeholder register 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.