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MonitoringFreemiumOpen Source
WHYLOGS
ML data logging and monitoring made simple
Apache-2.0
ABOUT
Machine learning models in production degrade over time due to data drift, concept drift, and data quality issues — but most teams lack visibility into what their models are seeing and predicting. Traditional monitoring solutions require sending raw data to external services, creating privacy and compliance risks. Whylogs generates statistical profiles of datasets (distributions, missing values, types) in a privacy-preserving manner, enabling teams to detect drift, track data quality, and debug model performance issues without exposing raw data.
INSTALL
pip install whylogsINTEGRATION GUIDE
1. Detect data drift between training and production datasets to catch model degradation early
2. Monitor data quality metrics (missing values, type ratios, distribution shifts) in ML pipelines
3. Track model performance over time and correlate changes with input distribution shifts
4. Generate privacy-preserving data profiles that can be shared without exposing raw customer data
5. Integrate ML monitoring into existing observability stacks with logging and alerting integrations
TAGS
ml-monitoringdata-qualitymodel-monitoringdata-driftobservabilitypythonlogging