AI model evaluation

Evaluate AI models with precision and context

Go beyond accuracy. Openlayer helps teams test AI models against real-world scenarios, edge cases, and evolving data.

Why evaluation and validation are different

Accuracy is just the beginning

Most teams evaluate models by looking at a single metric. But that’s not enough. You need to understand why a model performs the way it does, where it fails, and whether it’s ready to deploy.

Evaluation + validation = trust

A framework for AI model confidence

Run 100+ built-in or custom evaluation testsTap into an extensive library—or plug in your own—to vet accuracy, bias, safety, and domain-specific KPIs in minutes.

Compare model behavior across versionsDiff any two versions of the system side-by-side to pinpoint regressions and verify real-world gains.

Validate fairness, robustness, and edge-case performanceStress-test models on sensitive attributes, adversarial inputs, and long-tail scenarios before users ever notice.

Track evaluation outcomes over timeTrend pass/fail rates and metric deltas in a single dashboard to demonstrate continuous improvement (and compliance) with every release.

Built for all AI systems

Evaluate any model—ML, LLM, or hybrid

Tabular and time-series models

Generative AI systems (LLMs, RAG, agents)

Multimodal AI (CV, NLP, structured)

Custom workflows via API, SDK, or CLI

FAQs

Your questions, answered

What is AI model evaluation?

It's the structured analysis of model behavior, performance, and reliability using tests, metrics, and version comparisons.

How does Openlayer help with validation?

We enable custom test creation, drift analysis, fairness checks, and rollout comparisons—all in one platform.

What kinds of models can I evaluate?

Openlayer supports any type—ML, LLM, or multimodal—via flexible APIs and SDKs.

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