> For the complete documentation index, see [llms.txt](https://docs.hydrosphere.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hydrosphere.io/release-2.4.0/about/hydrosphere-features/automatic-outlier-detection.md).

# Automatic Outlier Detection

For each model with uploaded training data, Hydrosphere creates an outlier detection (Auto OD) metric, which assigns an outlier score to each request. A request is labeled as an outlier if the outlier score is greater than the 97th percentile of training data outlier scores distribution.

![](/files/-MJzwb2pHMFfORbM4TLF)

You can observe those models deployed as metrics in your monitoring dashboard. These metrics provide you with information about how novel/anomalous your data is.

If these values of the metric deviate significantly from the average, you can tell that you experience a data drift and need to re-evaluate your ML pipeline to check for errors.

## Supported Models

Right now Auto OD feature works only for Models with numerical scalar fields and uploaded training data.