Assurance Packs as a release artifact

Most teams treat a model release as “a new binary” (or a new endpoint). But in high-stakes settings, the thing you actually ship is: the model plus the evidence that it’s fit for purpose.

That’s the idea behind an Assurance Pack: a versioned bundle of artefacts that makes your release defensible.

The release artefact mindset

In software, a release produces:

• a build output (binary/container)
• a changelog
• tests + reports (CI output)

For ML, a release should produce:

• the model (weights/config)
• evaluation outputs (metrics + slices + failure notes)
• risk notes (what can go wrong, mitigations)
• monitoring plan (what you’ll watch, thresholds, actions, owners)
• traceability (what ran, on which data, from which commit)

An Assurance Pack is simply a standard way to package those things.

Why this helps (even before “compliance”)

Treating evidence as a release artefact forces two healthy disciplines:

1. Reproducibility by default
   If you can’t re-run the evaluation and re-create the summary, your release is fragile.

2. Diffs between releases
   When every release emits a pack, you can compare:

   • metric deltas
   • slice regressions
   • new failure modes
   • monitoring changes
   • policy/guardrail shifts

That turns “we changed the model” into “we can show what changed and why it’s acceptable”.

A lightweight sign-off flow

You don’t need a committee. Start with a simple rule:

• No pack → no deploy
• Pack produced → engineer + owner sign-off

A minimal sign-off checklist:

• Intended use / out of scope is still accurate
• Key metrics meet your thresholds
• No critical slice regression
• Monitoring plan updated for new risks
• Traceability complete (commit hash, data version, run ID)

How Ormedian thinks about packs

Our default bias is developer-first:

• packs should be folder-based (works with Git)
• should be buildable in CI
• should be diffable
• should be small enough to maintain, but strict enough to matter

What’s next

Over time, teams tend to want:

• pack templates per domain (vision, NLP, tabular)
• automated slice suites
• pack registries with approvals and audit trails

But the first win is simple: make evidence a standard output of every release.

If you’re building something high-stakes and want to adopt this early, join the waitlist — we’ll share templates and a reference implementation.