# Drift Detection Machine learning models do not extrapolate well outside of the training data distribution. In order to trust and reliably act on model predictions, it is crucial to monitor the distribution of incoming requests via different types of detectors. Drift detectors check when the distribution of the incoming requests is diverging from a reference distribution such as that of the training data. If data drift occurs, the model performance can deteriorate and it should be retrained. [Drift Detection example using CIFAR10](https://github.com/SeldonIO/seldon-core/blob/master/docs-gb/examples/drift_cifar10.html). The general framework shown in this example is to use the Seldon Core payload logger to pass requests to components that process them asynchronously. The results can be passed onwards to alterting systems. ![Example architecture](/files/QFvGFoWv0Cla6vPgHV0h) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.seldon.ai/seldon-core-1/configuration/integrations/drift_detection.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.