Welcome to Deepchecks!#
Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling you to thoroughly test your data and models from research to production.
We invite you to:
See the following Deepchecks Components section for more info about the Testing, CI, & Monitoring components and for links to their corresponding documentation.
Go to the Get Started with Deepchecks Monitoring section to have it up and running quickly and see it in action.
Deepchecks’ Components for Continuous Validation#
Deepchecks provides comprehensive support for your testing requirements, from examining data integrity and assessing distributions, to validating data splits, comparing models and evaluating their performance across the model’s entire development process.
Deechecks’ continuous validation approach is based on testing the ML models and data throughout their lifecycle using the exact same checks, enabling a simple, elaborate and seamless experience for configuring and consuming the results. Each phase has its relevant interfaces (e.g. visual outputs, python/json output results, alert configuration, push notifications, RCA, etc.) for interacting with the test results.
Get Started with Deepchecks Monitoring#
Deepchecks Monitoring takes the testing experience all the way to production: enabling you to send data over time, explore system status and receive alerts on problems that arise over time.
Get Help & Give Us Feedback#
Join Our Community 👋
In addition to perusing the documentation, feel free to:
Ask questions on the Slack Community.
Post an issue or start a discussion on Github Issues.
To support us, please give us a star on ⭐️ Github ⭐️, it really means a lot for open source projects!