Whylogs
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
About Whylogs
whylogs is an open-source library for logging data from machine learning models and data pipelines. It generates summaries called whylogs profiles that capture distributions, missing value counts, and configurable custom metrics.
Profiles can be used to track changes in a dataset, create data constraints, and visualize summary statistics. The library supports Python and Java, and profiles can be merged to aggregate data from distributed and streaming systems.
Key features
- Generate statistical profiles from Pandas DataFrames with why.log
- Track changes in datasets over time
- Create data constraints to check expected data shape
- Visualize key summary statistics from profiles
- Merge profiles to aggregate distributed or streaming data
- Configure custom metrics for text, image and other data
Best for: Data scientists, machine learning engineers, and data engineers monitoring data quality and model behavior.
Good for
- Profiles summarize datasets without requiring raw data storage
- Profiles can be merged across time periods and systems
- Supports logging in both Python and Java environments
Whylogs at a glance
- Website
- github.com/whylabs/whylogs
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Whylogs FAQ
What are whylogs profiles?
Summaries of datasets that capture distributions, missing value counts, and configurable custom metrics.
How do I log a Pandas DataFrame?
Install with pip install whylogs, then load the DataFrame and run why.log(df).
Can profiles from different batches be combined?
Yes, profiles are mergeable to form aggregate profiles for distributed, streaming, and multi-period data.
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