Whylogs: Statistical Profiles and Data Quality Metrics for ML Pipelines

Whylabs maintains whylogs, an open-source library for generating statistical profiles and data quality metrics for machine learning pipelines, updated in January 2025. It produces summaries of data distributions, including approximate statistics and constraints, to monitor model performance and data drift.

Use Cases

• Detecting data drift by comparing statistical-properties across different time windows
• Enforcing data-constraints within ML pipelines to ensure schema consistency
• Tracking model-performance metrics alongside data quality logs

Strengths

• Apache-2.0 licensed
• Uses approximate-statistics for high-volume data processing
• Updated as recently as January 2025

Limitations

• Contains statistical summaries only, not raw data observations
• Schema and metrics are dependent on user-defined pipeline implementation

Provenance

Source

whylabs via GitHub

Collection Method

Algorithmic generation via statistical sketching

Freshness

Updated as of January 2025