Machine Learning Project Work Breakdown Structure
This is a practical guide to building a work breakdown structure for a machine learning project project — a deliverable-oriented decomposition of the whole scope into manageable work packages, adapted to the realities of building and deploying a machine-learning model.
What a Work Breakdown Structure is
A work breakdown structure is a deliverable-oriented decomposition of the whole scope into manageable work packages. For the full concept and how it works in general, see Work Breakdown Structure. 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 work breakdown structure 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
- Top-level deliverables
- Sub-deliverables
- Work packages (8–80 hours)
- WBS numbering
- WBS dictionary entries
Machine Learning Project-specific considerations
Tailor the work breakdown structure 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 work breakdown structure 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.