What is it?

Evidently helps evaluate and monitor machine learning models in production. It generates interactive reports or JSON profiles from pandas DataFramesor csv files.

You can use visual reports for ad hoc analysis, debugging and team sharing, and JSON profiles to integrate Evidently in prediction pipelines or with other visualization tools.

Evidently currently works with tabular data. 6 reports are available.

​1. Data Drift​

Detects changes in feature distribution.

​2. Numerical Target Drift​

Detects changes in numerical target and feature behavior.

​3. Categorical Target Drift​

Detects changes in categorical target and feature behavior.

​4. Regression Model Performance​

Analyzes the performance of a regression model and model errors.

​5. Classification Model Performance​

Analyzes the performance and errors of a classification model. Works both for binary and multi-class models.

​6. Probabilistic Classification Model Performance​

Analyzes the performance of a probabilistic classification model, quality of the model calibration, and model errors. Works both for binary and multi-class models.

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