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Evidently Test Suites

Test data and models across the ML lifecycle.

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Evidently Test Suites

Run structured checks, and catch errors before users do. Understand and debug test results with visual reports.

Works where you work
Run Evidently wherever you run Python. Easily add to existing pipelines and DAGs.
Zero setup
Auto-generate Test Suites using reference dataset. No need to describe the data types or ranges – simply show an example.  
100s in-built checks
Check for nulls, stale features, range deviations, unexpected values, distribution drift, model quality, and more.
Evidently Data Stability Test preset

Ship with confidence

Run pre-deployment checks

Tick the boxes before you ship. Make sure your ML models and data are production-ready.

Evidently for pre-deployment checks
Evidently for ML pipelines validation

Validate production pipelines

Catch bad data, unseen values, or drift in ML pipelines. Stay ahead of trouble by preventing silent failures downstream.

CI/CD in machine learning

Run tests at every model update. Compare the metrics against the baseline, and make sure you train on high-quality data.

Evidently for CI/CD
Evidently for test-based ML monitoring

Test-based ML monitoring

Set up alerts, trigger model retraining, or generate visual reports based on the test results. Connect to the monitoring dashboards for complete visibility.

Run continuous tests

Data quality

Detect feature outages, bias, or instability in your data pipelines.

Data drift

Test for statistical distribution drift in model inputs and outputs.

ML performance

Catch unexpected drops in production model performance.

NLP and LLM

Verify the quality and track changes in unstructured text data.

Get started

Easily add Evidently to existing workflows, no matter where you deploy. 

Open-source

Run Evidently on your own.
Integrate in minutes and self-host.

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Cloud

Evidently Cloud platform is in private beta. Join our early access program to try it.

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