whylogs

Overview

An open-source library for logging data profiles from machine learning models and pipelines. It tracks data quality metrics and model performance over time while supporting privacy-preserving data collection.

Best for

Best for
Teams needing lightweight, privacy-aware data quality logging for ML pipelines

Use cases

• Monitor data drift in production ML pipelines
• Audit data quality before training or inference
• Log model predictions with statistical summaries

Notes

An open-source library for logging data profiles from machine learning models and pipelines. It tracks data quality metrics and model performance over time while supporting privacy-preserving data collection.

2,819 stars on GitHub. Last updated 2025-01-10. Licensed Apache-2.0.

Use cases

• Monitor data drift in production ML pipelines
• Audit data quality before training or inference
• Log model predictions with statistical summaries

Pros

• Open-source and community-backed
• Privacy-preserving data collection capabilities
• Tracks data quality and model performance over time

Cons

• Not a standalone monitoring solution, requires additional tooling for production deployment
• Limited to statistical profiling, no built-in alerting
• Relatively small community compared to larger observability platforms

Indexed from awesome-llmops and enriched against its public facts.