Give us a star ⭐️ on GitHub to support the project!
🚀 Join us January 25 for the Evidently monthly demo and Q&A. Register now →

Evidently Reports

Instantly analyze ML models and datasets.

Evidently Reports

Interactive reports on data and model performance. Metrics and dynamic visuals out of the box. Easy to use, easy to share.

Start in a minute
In your notebook or IDE, with a couple of lines of code.
Use presets or customize
Pick from curated presets and 100+ metrics, or add your own.
A simple, clean API
We wrote all the boilerplate. Define only what matters.
Evidently Data Drift report

Know your models

Explore and evaluate

Understand your data before you build and models before you deploy. Swiftly compare models, segments, and datasets side-by-side.

Evidently for data quality
Evidently for ML model cards

Create ML model cards

Document ML models and data. Customize the layout, add text annotations, and share with stakeholders.

Debug and troubleshoot

Find weak spots and segments of low performance. Speed up root cause analysis with pre-built plots and breakdowns.

Evidently for debugging

Get an in-depth look

Data quality

Assess data quality and understand your data with automated exploratory analysis.

Data drift

Evaluate shifts and changes in the input data distribution and model predictions.

ML performance

Explore the machine learning model quality and understand the errors it makes.


Extract signal from unstructured data to analyze text data properties and drift.

Get started

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

Evidently Cloud

Evidently Cloud is the easiest way to get ML monitoring up and running.



Deploy and run Evidently on your own.
Apache 2.0 license.

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.