Machine Learning Project Change Log
This is a practical guide to building a change log for a machine learning project project — a record of change requests and their decisions, adapted to the realities of building and deploying a machine-learning model.
What a Change Log is
A change log is a record of change requests and their decisions. For the full concept and how it works in general, see Change Log. 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 change log 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
- Change ID and description
- Impact assessment
- Decision and date
- Updated baselines
Machine Learning Project-specific considerations
Tailor the change log 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 change log 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.