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

• Aporia: Observability with customized monitoring and explainability for ML models.
• Arize: An end-to-end ML observability and model monitoring platform.
• Datatile: A library for managing, summarizing, and visualizing data.
• DataProfiler: A Python library designed to make data analysis, monitoring and sensitive data detection easy.
• 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: Interactive reports to analyze ML models during validation or production monitoring.
• Fiddler: Monitor, explain, and analyze your AI in production.
• Great Expectations: Helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.
• Manifold: A model-agnostic visual debugging tool for machine learning.
• Netron: Visualizer for neural network, deep learning, and machine learning models.
• Pandas Profiling: Extends the pandas DataFrame with df.profile_report() for quick data analysis.
• Pandera: A light-weight, flexible, and expressive data validation library for dataframes.
• Superwise: Fully automated, enterprise-grade model observability in a self-service SaaS platform.
• Whylogs: The open source standard for data logging. Enables ML monitoring and observability.
• ydata-quality: Data Quality assessment with one line of code.
• Yellowbrick: Visual analysis and diagnostic tools to facilitate machine learning model selection.
• Soda Core: Data profiling, testing, and monitoring for SQL accessible data.