Machine Learning Project Resource Plan
This is a practical guide to building a resource plan for a machine learning project project — the people and resources needed, when, and how they are allocated, adapted to the realities of building and deploying a machine-learning model.
What a Resource Plan is
A resource plan is the people and resources needed, when, and how they are allocated. For the full concept and how it works in general, see Resource Planning. 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 resource plan 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
- Roles and skills needed
- Allocation over time
- Capacity vs demand
- Resource calendar
Machine Learning Project-specific considerations
Tailor the resource plan 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 resource plan 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.