Test every ML model. Catch every regression.

ML evaluation

Test every ML model. Catch every regression.

Evaluate your ML models with 100+ customizable tests, version comparisons, and automated CI/CD validation.

Core features

Explore the platform

Validate all your models with tests

Push your models and data—Openlayer runs them against your test suite to catch issues fast. Choose from 100+ built-in tests or define your own with code or UI.

Track and test every change

Every new version gets tested automatically. Compare performance across releases to validate improvements, ensure consistency, and spot regressions.

Align all stakeholders

Openlayer makes model evaluation collaborative. Engineers, data scientists, and product managers can all define and interpret tests—reducing silos and improving outcomes.

Understand model behavior

Go beyond metrics. Use explainability tools to understand why your model made a prediction and debug issues with clarity and context.

Why it matters

Openlayer beats notebooks and dashboards

End-to-end coverage across model development and deploymentSee every experiment through to production with full-stack visibility and guardrails at each stage.

Automated testing tied to CI/CD pipelinesShip fast and safely—tests trigger automatically on every push and pull request.

Collaboration tools for broader alignmentGive data scientists, engineers, and stakeholders one shared workspace to discuss, review, and approve changes.

Intuitive UI + API-first experienceWork in a clean, no-code dashboard or drop straight into code—the same power, your preferred workflow.

Built for multimodal and complex ML architecturesHandle text, vision, tabular, or hybrid models at scale without re-tooling your stack.

Integrations

Works with your stack, not against it

Openlayer fits into your workflow with minimal effort. Use our SDKs or CLI to trigger tests, integrate with GitHub Actions or GitLab CI, and connect to cloud storage like S3 or data warehouses like BigQuery. No vendor lock-in. No friction.

Customer

Trusted by enterprise AI teams

“Openlayer transformed our ML workflow. We now catch issues days earlier and have confidence in every deployment.”

ML Lead at Fintech Company

FAQs

Your questions, answered

What is ML model evaluation?

ML model evaluation is the process of testing and analyzing a machine learning model’s performance across relevant metrics, data distributions, and edge cases to ensure it's reliable before deployment.

What makes Openlayer different from open-source scripts?

Openlayer provides structured, automated, and collaborative testing. Unlike ad hoc notebooks or scripts, we offer versioning, explainability, alerting, and integrations in one platform.

Can I test before production?

Yes, Openlayer integrates directly into your dev environment, so you can test before models ever reach production.

What types of models are supported?

We support all types—from tabular and NLP to computer vision and hybrid/multimodal systems.

$ openlayer push

ML evaluation