Drift & decay monitoring

How it works

Models don't only improve — fixes come undone, behaviours shift under silent updates, and accuracy on a task you relied on can decay between releases. Drift monitoring keeps a stable, versioned suite and re-runs it continuously, comparing each result against the baseline so a regression shows up as a trend, not a surprise in production. It is the longitudinal complement to benchmarking: benchmarks tell you where you stand, drift monitoring tells you which way you're moving.

When to use it

Long-lived systems on top of a model you don't control; tracking hosted models that update silently; guarding against regressions resurfacing after a fix; and as a standing safeguard for any system whose vendor doesn't publish a changelog for model updates, where drift monitoring is the only signal a behavior actually changed underneath you.

Limitations

Only as sensitive as the suite it re-runs — a drift in a capability the suite never exercised is invisible — and a drift signal flags that something moved without explaining why. Needs a stable baseline and disciplined versioning to avoid false alarms, and the suite itself has to be maintained as the product changes or it starts measuring the wrong thing.

Cite this