https://github.com/awesome-mlops/awesome-ml-monitoring

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀
https://github.com/awesome-mlops/awesome-ml-monitoring

List: awesome-ml-monitoring

aiops concept-drift data-cleaning data-drift data-management data-monitoring data-quality data-science dataops datascience deep-learning machine-learning machine-learning-platform mlops model-drift model-explainability model-management model-monitoring model-performance

Last synced: 15 days ago
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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 🚀

• Host: GitHub
• URL: https://github.com/awesome-mlops/awesome-ml-monitoring
• Owner: awesome-mlops
• License: apache-2.0
• Created: 2022-04-20T09:08:18.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-07T18:53:07.000Z (6 months ago)
• Last Synced: 2024-05-21T13:19:42.074Z (6 months ago)
• Topics: aiops, concept-drift, data-cleaning, data-drift, data-management, data-monitoring, data-quality, data-science, dataops, datascience, deep-learning, machine-learning, machine-learning-platform, mlops, model-drift, model-explainability, model-management, model-monitoring, model-performance
• Homepage:
• Size: 4.88 KB
• Stars: 51
• Watchers: 4
• Forks: 6
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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.