latest
Search…
⌃K
Links

Examples

Sample notebooks and tutorials

Sample notebooks

Simple examples on toy datasets to quickly explore what Evidently can do right out of the box.
Title
Jupyter notebook
Colab notebook
Contents
Evidently Test Presets
link
link
All pre-built Test Suites:
  • Data Drift
  • Data Stability
  • Data Quality
  • NoTargetPerformance
  • Regression
  • Classification (Multi-class, binary, binary top-K)
Evidently Tests
link
link
  • All individual tests (50+) that one can use to create a custom Test Suite.
  • How to set simple test parameters.
Evidently Metric Presets
link
link
All pre-built Reports:
  • Data Drift
  • Target Drift
  • Data Quality
  • Regression
  • Classification
Evidently Metrics
link
link
  • All individual metrics (30+) that one can use to create a custom Report.
  • How to set simple metric parameters.

Tutorials

To better understand potential use cases for Evidently (such as model evaluation and monitoring), refer to the detailed tutorials accompanied by the blog posts.
Title
Jupyter notebook
Colab notebook
Blog post
Data source
Monitor production model decay
link
link
Bike sharing UCI: link
Compare two models before deployment
link
link
HR Employee Attrition: link
Evaluate and visualize historical drift
link
link
Bike sharing UCI: link
Create a custom report (tab) with PSI widget for drift detection
link
link
---
California housing sklearn.datasets

Integrations

To see how to integrate Evidently in your prediction pipelines and use it with other tools, refer to the integrations.