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v0.1.57
v0.1.57
  • What is Evidently?
  • Installation
  • Get Started Tutorial
  • Reports
    • Data Drift
    • Data Quality
    • Numerical Target Drift
    • Categorical Target Drift
    • Regression Performance
    • Classification Performance
    • Probabilistic Classification Performance
  • Tests
  • Examples
  • Integrations
    • Evidently and Grafana
    • Evidently and Airflow
    • Evidently and MLflow
  • Features
    • Dashboards
      • Input data
      • Column mapping
      • Generate dashboards
      • CLI
      • Colab and other environments
    • Profiling
      • Input data
      • Column mapping
      • Generate profiles
      • CLI
    • Monitoring
  • User Guide
    • Customization
      • Select Widgets
      • Custom Widgets and Tabs
      • Options for Data / Target drift
      • Options for Quality Metrics
      • Options for Statistical Tests
      • Options for Color Schema
    • Recipes
  • SUPPORT
    • Contact
    • F.A.Q.
    • Telemetry
    • Changelog
  • GitHub Page
  • Website
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On this page
  • Sample notebooks
  • Tutorials
  • Integrations

Examples

Sample notebooks and tutorials

Sample notebooks

Here you can find simple examples on toy datasets to quickly explore what Evidently can do right out of the box. Each example shows how to create a default Evidently dashboard, a JSON profile and an HTML report.

Report
Jupyter notebook
Colab notebook
Data source

Data Drift + Categorical Target Drift (Multiclass)

Iris plants sklearn.datasets

Data Drift + Categorical Target Drift (Binary)

Breast cancer sklearn.datasets

Data Drift + Numerical Target Drift

California housing sklearn.datasets

Regression Performance

Classification Performance (Multiclass)

Iris plants sklearn.datasets

Probabilistic Classification Performance (Multiclass)

Iris plants sklearn.datasets

Classification Performance (Binary)

Breast cancer sklearn.datasets

Probabilistic Classification Performance (Binary)

Breast cancer sklearn.datasets

Data Quality

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

Compare two models before deployment

Evaluate and visualize historical drift

Create a custom report (tab) with PSI widget for drift detection

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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.

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Last updated 2 years ago

Bike sharing UCI:

Bike sharing UCI:

Bike sharing UCI:

HR Employee Attrition:

Bike sharing UCI:

Integrations
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How to break a model in 20 days. A tutorial on production model analytics.
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What Is Your Model Hiding? A Tutorial on Evaluating ML Models.
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How to detect, evaluate and visualize historical drifts in the data.
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