Machine Learning Project Project Plan
This is a practical guide to building a project plan for a machine learning project project — the integrated document that defines how the project is executed, monitored and controlled, adapted to the realities of building and deploying a machine-learning model.
What a Project Plan is
A project plan is the integrated document that defines how the project is executed, monitored and controlled. For the full concept and how it works in general, see Project Plan. 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 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
- Scope and deliverables
- Schedule and milestones
- Budget
- Roles and responsibilities
- Risk and communication approach
Machine Learning Project-specific considerations
Tailor the project 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 project 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.