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awesome-ml-monitoring

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A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

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# awesome-ml-monitoring

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

* [Aporia](https://www.aporia.com/): Observability with customized monitoring and explainability for ML models.
 * [Arize](https://github.com/Arize-ai/client_python): An end-to-end ML observability and model monitoring platform.
 * [Datatile](https://github.com/polyaxon/datatile): A library for managing, summarizing, and visualizing data.
 * [DataProfiler](https://github.com/capitalone/DataProfiler): A Python library designed to make data analysis, monitoring and sensitive data detection easy.
 * [Deepchecks](https://github.com/deepchecks/deepchecks): Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
 * [Evidently](https://github.com/evidentlyai/evidently): Interactive reports to analyze ML models during validation or production monitoring.
 * [Fiddler](https://www.fiddler.ai/): Monitor, explain, and analyze your AI in production.
 * [Great Expectations](https://github.com/great-expectations/great_expectations): Helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.
 * [Manifold](https://github.com/uber/manifold): A model-agnostic visual debugging tool for machine learning.
 * [Netron](https://github.com/lutzroeder/netron): Visualizer for neural network, deep learning, and machine learning models.
 * [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling): Extends the pandas DataFrame with df.profile_report() for quick data analysis.
 * [Pandera](https://github.com/pandera-dev/pandera): A light-weight, flexible, and expressive data validation library for dataframes.
 * [Superwise](https://www.superwise.ai): Fully automated, enterprise-grade model observability in a self-service SaaS platform.
 * [Whylogs](https://github.com/whylabs/whylogs): The open source standard for data logging. Enables ML monitoring and observability.
 * [ydata-quality](https://github.com/ydataai/ydata-quality): Data Quality assessment with one line of code.
 * [Yellowbrick](https://github.com/DistrictDataLabs/yellowbrick): Visual analysis and diagnostic tools to facilitate machine learning model selection.
 * [Soda Core](https://github.com/sodadata/soda-core): Data profiling, testing, and monitoring for SQL accessible data.

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