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whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction.
Features
- Detect data drift in model input features
- Detect training-serving skew, concept drift, and model performance degradation
- Validate data quality in model inputs or in a data pipeline
- Perform exploratory data analysis of massive datasets
- Track data distributions & data quality for ML experiments
- Enable data auditing and governance across the organization
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