Machine Learning Project Project Dashboard
This is a practical guide to building a project dashboard for a machine learning project project — a visual summary of the project’s key metrics and status, adapted to the realities of building and deploying a machine-learning model.
What a Project Dashboard is
A project dashboard is a visual summary of the project’s key metrics and status. For the full concept and how it works in general, see Project Dashboard. 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 dashboard 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
- Status indicators
- Schedule and cost KPIs
- Milestones
- Top risks/issues
Machine Learning Project-specific considerations
Tailor the project dashboard 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 dashboard 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.