Machine Learning Project RACI Matrix
This is a practical guide to building a raci matrix for a machine learning project project — a grid mapping tasks to who is Responsible, Accountable, Consulted and Informed, adapted to the realities of building and deploying a machine-learning model.
What a RACI Matrix is
A raci matrix is a grid mapping tasks to who is Responsible, Accountable, Consulted and Informed. For the full concept and how it works in general, see RACI Matrix. 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 raci matrix 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
- Activities as rows
- Roles as columns
- One Accountable per row
- Responsible / Consulted / Informed
Machine Learning Project-specific considerations
Tailor the raci matrix 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 raci matrix 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.