alibi-detect - Algorithms for outlier, adversarial and drift detection

•    Python

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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. For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect.

time-series text images detection tabular-data semi-supervised-learning anomaly unsupervised-learning adversarial concept-drift outlier drift-detection data-drift

evidently - Interactive reports to analyze machine learning models during validation or production monitoring

•    Jupyter

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Interactive reports and JSON profiles to analyze, monitor and debug machine learning models. Evidently helps evaluate machine learning models during validation and monitor them in production. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. You can use visual reports for ad hoc analysis, debugging and team sharing, and JSON profiles to integrate Evidently in prediction pipelines or with other visualization tools.

data-science machine-learning pandas-dataframe jupyter-notebook html-report production-machine-learning mlops model-monitoring machine-learning-operations data-drift