# 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/-ML7EbKIRwZCb18SMfJ6)

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.


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