# Overview

![Alibi Detect Logo](https://3439768760-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FepFpHcWRG4i4ZeWUX6zn%2Fuploads%2Fgit-blob-0f1058925f9ad4063107b59ae4c7c7548f3461d7%2FAlibi_Detect_Logo_RGB_Colour_Blue.png?alt=media)

## Alibi Detect

[Alibi Detect](https://github.com/SeldonIO/alibi-detect) is a source-available Python library focused on **outlier**, **adversarial** and **drift** detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both **TensorFlow** and **PyTorch** backends are supported for drift detection.

For more background on the importance of monitoring outliers and distributions in a production setting, check out [this talk](https://slideslive.com/38931758/monitoring-and-explainability-of-models-in-production?ref=speaker-37384-latest) from the *Challenges in Deploying and Monitoring Machine Learning Systems* ICML 2020 workshop, based on the paper [Monitoring and explainability of models in production](https://arxiv.org/abs/2007.06299) and referencing Alibi Detect.

For a thorough introduction to drift detection, check out the talk below titled, [Protecting Your Machine Learning Against Drift: An Introduction](https://youtu.be/tL5sEaQha5o). The talk covers what drift is and why it pays to detect it, the different types of drift, how it can be detected in a principled manner and also describes the anatomy of a drift detector.

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