Machine Learning Project Business Case
This is a practical guide to building a business case for a machine learning project project — the justification that weighs the costs, benefits and risks of doing the project, adapted to the realities of building and deploying a machine-learning model.
What a Business Case is
A business case is the justification that weighs the costs, benefits and risks of doing the project. For the full concept and how it works in general, see Business Case. 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 business case 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
- Problem or opportunity
- Options considered
- Costs and benefits
- Financial appraisal (ROI/NPV/IRR)
- Recommendation
Machine Learning Project-specific considerations
Tailor the business case 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 business case 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.