Machine Learning Project Project Checklist
This is a practical guide to building a project checklist for a machine learning project project — a step-by-step checklist covering the project from start to finish, adapted to the realities of building and deploying a machine-learning model.
What a Project Checklist is
A project checklist is a step-by-step checklist covering the project from start to finish. For the full concept and how it works in general, see Project Management Checklist. 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 checklist 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
- Initiation checks
- Planning checks
- Execution checks
- Closure checks
Machine Learning Project-specific considerations
Tailor the project checklist 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 checklist 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.