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ML Monitoring

Keep tabs on model and data health in production.

Evidently ML Monitoring

Get real-time visibility, detect issues, and fix them fast. This is a complete open-source solution with a logging module and user interface to stay on top of your production ML models.

Integrate easily
Use the Evidently Python library to capture what’s going on.
Track what you want
Log statistical data summaries, test results, or specific metrics. Start with presets or customize.
Any model, anywhere
Connect to your batch ML pipelines, real-time services, or push on-demand. 
Evidently Data Drift preset

A central hub for ML in production


Get a live view of all your production ML systems. Track performance, data quality, and drift consistently. Know when it's time to intervene or retrain the models.

Evidently for monitoring ML systems
Evidently for exploring drift


Drill down to specific periods, features, or data segments. Understand environment changes to make sure your models never go stale.


Take action once you spot issues. Get to the root cause faster with pre-built summaries and plots. Switch flexibly from monitoring UI to Jupyter notebook.

Evidently for debugging
Evidently for collaborating on ML systems


Share insights with the team and stakeholders. Communicate the value the model brings and how well it works. Boost collaboration and build trust in ML systems.

Get full visibility

Data quality

Capture and track data summaries. Monitor compliance with data quality KPIs over time.

Data drift

Monitor input and output distributions. Take action when the drift score exceeds limits.

ML performance

Detect model drift, underperforming segments, and changes in the model target.


Monitor embeddings, unstructured text data, and quality of LLM and NLP-powered systems.

Get started

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


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



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

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