Deepchecks Continuous Validation: Testing, CI & Monitoring

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:

Deepchecks Suite Run

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.

Testing Docs (here)

Tests during research and model development

CI Docs

Tests before deploying the model to production

Monitoring Docs

Tests and continuous monitoring during production

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.

Phases for Continuous Validation of ML Models and Data

Get Started with Deepchecks Testing#

πŸƒβ€β™€οΈ Quickstarts πŸƒβ€β™€οΈ

Downloadable end-to-end guides, demonstrating how to start testing your data & model in just a few minutes.

πŸ’β€β™‚οΈ Get Help & Give Us Feedback πŸ’

Links for how to interact with us via our Slack Community or by opening an issue on Github.

πŸ’» Install πŸ’»

Full installation guide (quick one can be found in quickstarts)

πŸ€“ General: Concepts & Guides πŸ€“

A comprehensive view of deepchecks concepts, customizations, and core use cases.

πŸ”’ Tabular πŸ”’

Quickstarts, main concepts, checks gallery and end-to-end guides demonstrating how to start working Deepchecks with tabular data and models.

πŸ”€οΈ NLP πŸ”€οΈ

Quickstarts, main concepts, checks gallery and end-to-end guides demonstrating how to start working Deepchecks with textual data. Future releases to come!

πŸŽ¦β€ Computer Vision (Note: in Beta Release) πŸŽ¦β€

Quickstarts, main concepts, checks gallery and end-to-end guides demonstrating how to start working Deepchecks with CV data and models. Built-in support for PyTorch, TensorFlow, and custom frameworks.

πŸš€ Interactive Checks Demo πŸš€

Play with some of the existing tabular checks and see how they work on various datasets with custom corruptions injected.

πŸ€– API Reference πŸ€–

Reference and links to source code for Deepchecks Testing’s components.

πŸƒβ€β™€οΈ Testing Quickstarts πŸƒβ€β™€οΈ#

πŸ”’ Tabular πŸ”’
πŸ”€οΈ NLP πŸ”€οΈ
πŸŽ¦β€ Vision πŸŽ¦β€ (in Beta)

Get Help & Give Us Feedback#

Join Our Community πŸ‘‹

In addition to perusing the documentation, feel free to:

To support us, please give us a star on ⭐️ Github ⭐️, it really means a lot for open source projects!