# Data Drift Detection

> Monitor changes in real-world data distributions

When machine learning models are deployed in production, sometimes even minor changes in a data distribution can adversely affect the performance of ML models. When the input data distribution shifts then prediction quality can drop. It is important to track this kind of data drift. Drift detection in Seldon Enterprise Platform is powered by our open source library `alibi-detect`. Learn more about the data drift detection concepts on the [alibi-detect documentation page](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/background.html).

## Available Detection Methods

Seldon Enterprise Platform supports a subset of the methods currently available in `alibi-detect` for Seldon Core deployments - currently only offline drift detection on input data streams to ML models are supported. Seldon Enterprise Platform enables monitoring of drift detection metrics in real-time and on historical data, both at a feature-level and batch-level as per the detection method. Find documentation on the supported `alibi-detect` drift detection methods in the table below.

| Drift detection method         | Detection Type | Alibi-Detect docs                                                                                     |
| ------------------------------ | -------------- | ----------------------------------------------------------------------------------------------------- |
| Kolmogorov-Smirnov Drift       | Offline        | [Method Docs](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/methods/ksdrift.html)         |
| ChiSquare Drift                | Offline        | [Method Docs](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/methods/chisquaredrift.html)  |
| Maximum Mean Discrepancy Drift | Offline        | [Method Docs](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/methods/mmddrift.html)        |
| Tabular Drift                  | Offline        | [Method Docs](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/methods/tabulardrift.html)    |
| Classifier Drift               | Offline        | [Method Docs](https://docs.seldon.io/projects/alibi-detect/en/latest/cd/methods/classifierdrift.html) |

### Demo

Try out the [drift detection and distribution comparison demo](https://docs.seldon.ai/seldon-enterprise-platform/demos/seldon-core-v1/drift-detection) on mixed-type tabular data in Seldon Enterprise Platform.