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With `whylogs`, users are able +to generate summaries of their datasets (called `whylogs` profiles) which they can use to: + +1. Track changes in their dataset +2. Create data constraints to know whether their data looks the way it should +3. Quickly visualize key summary statistics about their datasets + +These three functionalities enable a variety of use cases for data scientists, +machine learning engineers, and data engineers: + +- 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 +- Standardize data documentation practices across the organization + +--- + +## Why logs? (Cont'd) + +WhyLabs is a managed service offering built for helping users make the most of their `whylogs` profiles. +With WhyLabs, users can ingest profiles and set up automated monitoring as well as gain full observability +into their data and ML systems. With WhyLabs, users can ensure the reliability of their data and models, +and debug any problems that arise with them. + +/flask_whylogs_whylabs_flow