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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.

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Evidently ML monitoring dashboard

How are your models doing in production?

GitHub stars
Evidently AI - Open-source monitoring for machine learning models | Product Hunt

Model Quality

Evaluate the ML model quality, and go beyond aggregate performance to discover where it fails.

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Evidently Model Quality report
Evidently Data Drift report

Data Drift

Run statistical tests to compare the input feature distributions, and visually explore the drif.

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Target Drift

Understand how model predictions and target behavior change over time.

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Evidently Target Drift report
Evidently Data Quality report

Data Quality

Get a snapshot of data health, and drill down to explore feature behavior and statistical properties.

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How it works

Evidently dashboard

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.

Evidently as a part of a pipeline

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.

ML monitoring service

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

Join our Discord Community!

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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Get started

$ pip install evidently

Check the complete documentation.

Long live models!

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