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How are your models doing in production? Make sure that your ML systems work reliably in real-world scenarios.
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The ultimate goal of an ML system is to improve or automate business processes. To keep the models on track, you need to measure their production performance and understand the errors they make.
Evidently helps track ML model quality with built-in standard metrics, checks, and visualizations. You can run pre-deployment model testing, monitor them in production and troubleshoot when things go wrong.
Evaluate the performance and compare ML models side-by-side. Understand key metrics, such as accuracy, precision, and recall. Go beyond aggregates to explore where models fail.
Validate the quality and behavior of ML models with structured checks. Ensure the models comply with your expectations when you deploy, retrain and update them.
Keep a close eye on the health of production ML systems. Maintain confidence in your models and know when to intervene.
Track metrics over time and detect deviations. Take proactive steps by monitoring prediction and data drift even before you get the labels.
Choose from standard model quality metrics for different model types or easily define your own. Align the evaluation with your business goals.
Easily add Evidently to existing workflows, no matter where you deploy.
