deepchecks
Enrichment pendingDeepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and model
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Overview
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Capability facts
- Languages
- python
Source: github.language · Jul 11, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
pip install deepchecks -U --userSource link
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README
⏩ Getting Started
💻 Installation
Deepchecks Testing (and CI) Installation
pip install deepchecks -U --user
For installing the nlp / vision submodules or with conda:
- For NLP: Replace
deepcheckswith"deepchecks[nlp]", and optionally install alsodeepchecks[nlp-properties] - For Computer Vision: Replace
deepcheckswith"deepchecks[vision]". - For installing with conda, similarly use:
conda install -c conda-forge deepchecks.
Check out the full installation instructions for deepchecks testing here.
Deepchecks Monitoring Installation
To use deepchecks for production monitoring, you can either use our SaaS service, or deploy a local instance in one line on Linux/MacOS (Windows is WIP!) with Docker. Create a new directory for the installation files, open a terminal within that directory and run the following:
pip install deepchecks-installer
deepchecks-installer install-monitoring
This will automatically download the necessary dependencies, run the installation process and then start the application locally.
The installation will take a few minutes. Then you can open the deployment url (default is http://localhost), and start the system onboarding. Check out the full monitoring open source installation & quickstart.
Note that the open source product is built such that each deployment supports monitoring of a single model.