Monitoring is boring
We build tools to evaluate, test and monitor machine learning models, so you don't have to.
Yes, it is open source.
How are your models doing in production?
Model Quality
Evaluate the ML model quality, and go beyond aggregate performance to discover where it fails.
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Data Drift
Run statistical tests to compare the input feature distributions, and visually explore the drift.
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Data Quality
Get a snapshot of data health, and drill down to explore feature behavior and statistical properties.
GET STARTEDHow it works
As a dashboard
Generate interactive reports in the notebook or export them as an HTML file. Use them for visual evaluation, debugging and sharing with the team.
As part of the pipeline
Run the data and model checks as part of the pipeline. Integrate with tools like Mlflow or Airflow to schedule the tests and log the results.
As a monitoring service
Collect the model quality metrics from the deployed ML service. Currently works through integration with Prometheus and Grafana.
What the community says
“Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!”
Ben Wilson
Principal RSA, Databricks
“I love the plug-and-play features for monitoring ML models.”
Emmanuel Raj
Senior Machine Learning Engineer, TietoEVRY
“Evidently is a first-of-its-kind monitoring tool that makes debugging machine learning models simple and interactive. It's really easy to get started!”
Niklas von Maltzahn
Head of Decision Science, JUMO
“I was searching for an open-source tool, and Evidently perfectly fit my requirement for model monitoring in production. It was very simple to implement, user-friendly and solved my problem!”
Manoj Kumar
Data Scientist, Walmart Labs
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If you need help in using the tool or want to chat about doing ML in production, we have a place for that!
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