Profile and monitor your ML data pipeline end-to-end, and so much more. This page is updated frequently, and contains different external resources to help you be successful when working with whylogs.
You can find various examples whylogs examples in GitHub here: https://github.com/whylabs/whylogs-examples
You can get started with whylogs open-source by following the links:
You can experience the WhyLabs AI Observability Platform in via our Sandbox experience for free. The sandbox provides real-time insights using the WhyLabs Platform. It monitors a model in production and is continuously updated with live data.
- You can click here to try the WhyLabs Platform Sandbox.
- Alternatively, you can click here to book a live demo.
We're excited that you'd like to be part of the community and contribute to whylogs.
We'll add detailed instructions on how to contribute soon, but for now you follow these steps to contribute:
To join, please go to https://whylabs.ai/slack-community.
Then drop by the
#java channel to introduce yourself 👋
You can find instructions on how to get started with whylogs here: https://github.com/whylabs/whylogs. Small changes that don't need to be tested locally--such as for documentation--can be made directly through GitHub.
The best place to start is by checking existing issues in Github, to identify the type of contribution you'd like to make. When you've found an issue, please comment on it to let everyone know you're working on it.
If there's no issue for what you'd like to work on please go ahead and create one. And again, add a comment to let everyone you're working on it.
Make sure to take a look at the Code of Conduct, and when your changes are ready run through our contribution checklist.
If you have any questions about how to contribute, just ask the community on Slack!