GraphCanon updated 3w · GitHub synced 3w
Decision brief
whylogs is an open-source data logging library that provides detailed data quality monitoring and model performance tracking over time. It supports privacy-preserving data collection to ensure robust and safe operations.
Good fit when
- When you need comprehensive data visibility to track changes in datasets and model input features for ML systems
- For quick identification of issues like training-serving skew, concept drift, and model performance degradation
Avoid when
- When limited to using closed-source tools as whylogs is open source under the Apache-2.0 license
- In environments where privacy-preserving features are not required or can be handled through other means
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (2,830) are secondary evidence.
- Docker Hub pulls (30d)
- 10,953·Docker Hub API·3w
Maintenance and security
Full trust report- Maintenance
- Dormant (568d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/whylabs/whylogsSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides visibility into data quality and model performance over time. Supports privacy-preserving data collection to ensure safety and robustness.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 2, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 2, 2026
- Languages
- jupyter notebook
Source: github.language · Aug 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 2, 2026)
<a href="https://github.com/whylabs/whylogs#python-quickstart"><b>Python Quickstart</b></a> •Source link
Tags
README
The open standard for data logging
Documentation • Slack Community • Python Quickstart • WhyLabs Quickstart
What is whylogs
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
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
- And more
If you have any questions, comments, or just want to hang out with us, please join our Slack Community. In addition to joining the Slack Community, you can also help this project by giving us a ⭐ in the upper right corner of this page.
Python Quickstart
Installing whylogs using the pip package manager is as easy as running pip install whylogs in your terminal.
From here, you can quickly log a dataset:
import whylogs as why
import pandas as pd
#dataframe
df = pd.read_csv("path/to/file.csv")
results = why.log(df)
And there you have it, you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, read on.
Table of Contents
- whylogs Profiles
- Data Constraints
- Profile Visualization
- Integrations
- Supported Data Types
- Examples
- Usage Statistics
- Community
- Contribute
whylogs Profiles
What are profiles
whylogs profiles are the
For agents
This page has a .md twin and JSON over the API.