Welcome to Deepchecks!#
Deepchecks is a holistic tool for testing, validating and monitoring your machine learning models and data, throughout the model’s lifecycle. It enables you to identify problems with your data quality, distributions, and model’s performance with minimal effort.
See more info in the Deepchecks Components for Continuous Validation section, along with the direct links to the documentation of each component.
Get Started with Deepchecks Testing#
Links for how to interact with us via our Slack Community or by opening an issue on Github.
🏃♀️ Testing Quickstarts 🏃♀️#
Deepchecks’ Components#
Deepchecks accompanies you through various testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models, throughout the model’s lifecycle.
Deechecks’ continuous validation approach is based on testing the ML models and data throughout the different phases 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, output results to json, alert configuration, push notifications, RCA, etc.) for interacting with the test results.
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 contribute to the package, check out the Contribution Guidelines and join the contributors-q-and-a channel on Slack, or communicate with us via github issues.
To support us, please give us a star on ⭐️ Github ⭐️, it really means a lot for open source projects!