# Examples
## Quick start
This is the best place to start.
| Title | Code example | Guide |
| -------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Quickstart for Reports and Test Suites | Jupyter notebook
Colab
| [Tutorial](https://docs.evidentlyai.com/get-started/tutorial) |
| Quickstart for ML Monitoring | [Python script](https://github.com/evidentlyai/evidently/blob/main/examples/sample_notebooks/get_started_monitoring.py) | [Tutorial](https://docs.evidentlyai.com/get-started/tutorial-monitoring) |
## Sample notebooks
Simple examples show different Metrics, Tests and Presets. Head to Colab examples to see the pre-rendered visuals.
| 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.
|
For more examples showing specific features, including text data support, head to the "How to" examples.
{% content-ref url="user-guide/how-to-guides" %}
[how-to-guides](https://francesco.gitbook.io/docs.evidentlyai.com/user-guide/how-to-guides)
{% endcontent-ref %}
## Tutorials
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](https://github.com/francesco086/evidently/blob/doc-integration-dvclive/examples/data_stories/bicycle_demand_monitoring.ipynb) | [How to break a model in 20 days. A tutorial on production model analytics.](https://evidentlyai.com/blog/tutorial-1-model-analytics-in-production) |
| Compare two ML models before deployment (classification example) | [Jupyter notebook](https://github.com/francesco086/evidently/blob/doc-integration-dvclive/examples/data_stories/ibm_hr_attrition_model_validation.ipynb) | [What Is Your Model Hiding? A Tutorial on Evaluating ML Models.](https://evidentlyai.com/blog/tutorial-2-model-evaluation-hr-attrition) |
| Evaluate and visualize historical data drift | [Jupyter notebook](https://github.com/francesco086/evidently/blob/doc-integration-dvclive/examples/integrations/mlflow_logging/historical_drift_visualization.ipynb) | [How to detect, evaluate and visualize historical drifts in the data.](https://evidentlyai.com/blog/tutorial-3-historical-data-drift) |
| Monitor NLP models in production | [Colab](https://colab.research.google.com/drive/15ON-Ub_1QUYkDbdLpyt-XyEx34MD28E1) | [Monitoring NLP models in production: a tutorial on detecting drift in text data](https://www.evidentlyai.com/blog/tutorial-detecting-drift-in-text-data) |
| Create ML model cards | [Jupyter notebook](https://github.com/evidentlyai/community-examples/tree/main/tutorials/How_to_create_an_ML_model_card.ipynb) | [A simple way to create ML Model Cards in Python](https://www.evidentlyai.com/blog/ml-model-card-tutorial) |
| Use descriptors to monitor text data | [Jupyter notebook](https://github.com/evidentlyai/community-examples/tree/main/tutorials/How_to_add_a_custom_text_descriptor.ipynb) | [Monitoring unstructured data for LLM and NLP with text descriptors](https://www.evidentlyai.com/blog/unstructured-data-monitoring) |
You can find more examples in the [Community Examples](https://github.com/evidentlyai/community-examples) repository.
### Integrations
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" %}
[evidently-integrations](https://francesco.gitbook.io/docs.evidentlyai.com/integrations/evidently-integrations)
{% endcontent-ref %}