# 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.

![](https://1529109172-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MESaD8WY3ggQLtBByXl%2Fsync%2F26be5c07700115b90ae936091ec596cf415cbfcf.gif?generation=1603095672244701\&alt=media)

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.