Monitor performance, detect drift, and catch production issues before they impact users. Openlayer offers a modern monitoring layer designed for real-world ML systems.
Why model monitoring still matters
Production models don’t fail loudly, they fade quietly
Even well-trained models degrade over time. Upstream data changes, shifting user behavior, or infrastructure issues can silently erode performance.
That’s why model monitoring is a key layer of any resilient AI system.
Openlayer's approach to model monitoring
A monitoring layer that's flexible, observable, and reliable
Where monitoring fits in the lifecycle
Not just post-production, but part of a test-first workflow
Openlayer integrates monitoring into a broader evaluation lifecycle—from pre-deployment validation to post-deployment oversight.
Less firefighting
More control
Faster iteration
Why not just use a dashboard?
Dashboards show you what broke. Openlayer shows you why.
You don’t need more alerts, you need insight. Openlayer connects model failures to grounded measurements.
Input drift
Failing test cases
Version regressions
Who it's for
Designed for ML engineers and platform teams
If you’re responsible for model reliability at scale, Openlayer’s model monitoring layer fits your workflow. Whether managing a handful of models or hundreds, observability should be automated—not ad hoc.
FAQs
Your questions, answered
$ openlayer push