A practical specification for generating a versioned assurance pack for ML systems: evaluation evidence, traceability, and monitoring plans — built for engineers.
A bundle of artefacts produced from a model + data + evaluation pipeline that answers:
- What is this system for (and not for)?
- How was it trained and evaluated (reproducibly)?
- What can go wrong (and what mitigations exist)?
- How will it be monitored post-deployment?
Many teams can train models. Fewer can produce audit-ready evidence that stays current as models change.
- A clear pack structure + naming
- Minimal “must-have” sections for traceability + evaluation + monitoring
- Templates that teams can adopt without new platforms
Pre-alpha spec. This repo is intentionally docs-first.
- Next: reference implementation (CLI) after the spec stabilises.
Email: [email protected]