description
Code examples and tutorials.

Quick Start

Check the short Quickstart examples here.

Get Started Tutorials

Introductory tutorials that walk you through the basic functionality step by step.

Title	Guide	Code
LLM Evaluation	Tutorial	Jupyter notebook
Data & ML Monitoring	Tutorial	Jupyter notebook
LLM Tracing	Tutorial	Jupyter notebook
Intro to Reports & Test Suites (OSS)	Tutorial	Jupyter notebook
Self-host ML monitoring Dashboard (OSS)	Tutorial	Jupyter notebook

Simple examples show different local evaluations (Metrics, Tests and Presets) for tabular data and ML.

Title	Code example	Contents
Evidently Test Presets	Jupyter notebook Colab	Pre-built Test Suites on tabular data: Data Drift Data Stability Data Quality NoTargetPerformance Regression Classification (Multi-class, binary, binary top-K)
Evidently Tests	Jupyter notebook Colab	All individual Tests (50+) that one can use to create a custom Test Suite. Tabular data examples. How to set test conditions and parameters.
Evidently Metric Presets	Jupyter notebook Colab	All pre-built Reports: Data Drift Target Drift Data Quality Regression Classification
Evidently Metrics	Jupyter notebook Colab	All individual metrics (30+) that one can use to create a custom Report. How to set simple metric parameters.
Evidently LLM Metrics	Jupyter notebook	Evaluations for Text Data and LLMs

For LLM and text metrics, check the LLM evaluation tutorial.

Title	Tutorial
How to create LLM judge evaluator	Tutorial
How to run regression testing for LLM products	Tutorial

To better understand the Evidently use cases, refer to the detailed tutorials accompanied by the blog posts.

Title	Code example	Blog post
Understand ML model decay in production (regression example)	Jupyter notebook	How to break a model in 20 days. A tutorial on production model analytics.
Compare two ML models before deployment (classification example)	Jupyter notebook	What Is Your Model Hiding? A Tutorial on Evaluating ML Models.
Evaluate and visualize historical data drift	Jupyter notebook	How to detect, evaluate and visualize historical drifts in the data.
Monitor NLP models in production	Colab	Monitoring NLP models in production: a tutorial on detecting drift in text data
Create ML model cards	Jupyter notebook	A simple way to create ML Model Cards in Python
Use descriptors to monitor text data	Jupyter notebook	Monitoring unstructured data for LLM and NLP with text descriptors

You can find more examples in the Community Examples repository.

For code examples on specific functionality, check the How-To examples:

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

{% content-ref url="../integrations/evidently-integrations.md" %} integrations/evidently-integrations.md {% endcontent-ref %}