Deepchecks

Deepchecks is an open-source platform for continuous validation of AI and machine learning models and data, maintained on GitHub with about 4,100 stars. It lets teams thoroughly test datasets and models from research through production with a large library of built-in checks and visual reports.

Overview

At its core, Deepchecks runs Checks: individual tests that look for specific problems such as data drift, leakage, weak performance segments or integrity issues. Checks can be grouped into Suites that produce a single concluding report, and they work across tabular data, NLP and computer vision. Teams use Deepchecks during development, wire it into CI pipelines to gate model releases, and run its monitoring components to watch deployed models over time and catch degradation early. The project is under active development, with updates continuing into 2026.

Key Features

• Large library of built-in checks for tabular, NLP and vision data
• Suites that combine checks into one pass/fail validation report
• HTML and Jupyter reports for inspecting results visually
• CI integration for automated model testing before deployment
• Production monitoring components with alerts on drift and performance
• Custom check API for writing your own validations in Python

💳 Pricing

The core Deepchecks packages are free and open source under AGPL 3.0. The company also offers paid products: a commercial edition of the monitoring platform with premium enterprise features and managed offerings, so teams can start open source and upgrade when they need collaboration, scale or support.

Who Is It For

Deepchecks is for ML engineers, data scientists and MLOps teams who need systematic testing and monitoring to ship models safely and keep them reliable in production.