alibi-detect (conda): [Alibi Detect](https://github.com/SeldonIO/alibi-detect) is an open source 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.  - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/)  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 [Protecting Your Machine Learning Against Drift: An Introduction](https://youtu.be/tL5sEaQha5o). he 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.   PyPI: [https://pypi.org/project/alibi-detect/](https://pypi.org/project/alibi-detect/)
Keywords: adversarial|anomaly|concept-drift|data-drift|detection|drift-detection|images|outlier|semi-supervised-learning|tabular-data|text|time-series|unsupervised-learning