# ML/AI Projects

ML/AI delivery needs separate treatment for data, experimentation, model behaviour, and operations.

## Workstreams

- problem framing
- data exploration and quality
- experimentation and evaluation
- deployment and monitoring
- responsible-AI review

## Practical Rule

Do not let model work collapse into a generic software backlog item.
Each stage needs explicit assumptions, evidence, and exit criteria.
