whylabs/whylogs

Health v2 · maintenance only

Adoption not part of the score

2831 stars · 144 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

whylogs is an open-source Python data logging library for machine learning models and data pipelines. It profiles datasets with privacy-preserving approximate statistics to track data quality and model performance over time.

Use cases

• profile datasets to monitor data quality in ML pipelines
• detect data drift between training and production data
• log model performance metrics over time
• collect privacy-preserving data statistics without storing raw data
• validate data against quality constraints before training
• integrate data observability into MLOps workflows

When to choose

• you need lightweight, privacy-preserving data profiling for ML pipelines
• you want to track data drift and model performance over time
• you're building MLOps observability and want an open-source profiling standard

When to avoid

• you need full-fidelity storage of raw data rather than statistical profiles
• you need a complete commercial monitoring dashboard out of the box
• your stack is not Python-based

Facets

library · maturity active

logging monitoring analytics data-science machine-learning machine-learning data-science monitoring analytics python cross-platform data-logging data-quality mlops data-profiling drift-detection privacy-preserving approximate-statistics model-monitoring data-engineering

Member repositories

For agents

markdown · JSON · MCP: product_card(name="whylabs/whylogs")