deepchecks

Why we included this project

Deepchecks is for teams that have watched a model degrade in production and wish they had caught it earlier. It packages validation as a Python library of tests for data quality, data drift, model performance, and label issues, run directly against your pandas dataframes and trained models. You can use it interactively in notebooks, generate HTML reports to share with stakeholders, or wire the same suites into CI so regressions surface before release. Rather than covering one slice of the lifecycle, it spans research through deployment, which makes it useful both for data scientists who want ready-made checks and MLOps engineers who need a consistent, documented way to gate releases.