ML testing that catches issues before they catch you

ML testing

ML testing that catches issues before they catch you

From data validation to behavioral testing, Openlayer helps you test every aspect of your ML system—before and after deployment.

Why ML testing matters

You can't trust a model you haven't tested

ML models fail silently—especially when inputs change, data drifts, or edge cases arise. Testing helps you prevent costly failures and ensures models work as expected under real-world conditions.

What should ML testing include?

More than just accuracy checks

Behavioral tests for edge casesProbe long-tail, adversarial, and safety-critical scenarios to make sure the model behaves when inputs get weird.

Drift detection on data and predictionsTrack upstream features and downstream outputs for distribution shifts so you can respond before accuracy craters.

Fairness and bias checksAudit performance across demographic slices and sensitive attributes to surface disparate impacts early.

Test coverage tracking across datasetsKnow exactly which classes, segments, and failure modes each suite exercises—and where the blind spots remain.

Version-to-version regression testingCompare new checkpoints against the last good build to catch unintended regressions in a single report.

Test automation tied to CI/CDRun the whole battery on every pull request or scheduled job so quality gates never rely on manual heroics.

Openlayer's approach to ML testing

Testing built into every commit

Openlayer integrates testing into your ML development lifecycle—automatically running tests on every push and tracking changes across models, prompts, and data versions.

Tabular

NLP

Vision

Multimodal systems

FAQs

Your questions, answered

What is ML testing?

What tools are used for ML testing?

What types of tests can I run in Openlayer?

$ openlayer push

ML testing