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deepchecks

deepchecks/deepchecks

Tests for Continuous Validation of ML Models & Data

GitHub Website

GraphCanon updated 2w · GitHub synced 2w

4.0k stars301 forksLast push 7mo Python Other

Decision brief

Deepchecks offers open-source solutions for continuous validation of machine learning models from research to production with features like data drift detection, model monitoring, and HTML reporting.

Good fit when

You need continuous validation tools that support all stages from research to production
Your project relies on Python libraries like pandas or PyTorch, needing specialized deepchecks modules for better integration

Avoid when

Your team lacks the expertise to run and interpret monitoring outputs in an open-source environment
Your use case involves a need to monitor more than one model without expanding beyond the limitations of Deepchecks open source

Observed Jul 17, 2026 · Source: enrich:decision_facts

deepchecks alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Slowing (216d since push); As of 2w
Provenance: Not a fork · Organization account; As of 2w
Security (OSV): No lockfile; As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install deepchecks

PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Deepchecks is an open-source solution for validation needs at all stages of AI projects from research to production.

Capability facts

Languages: python
Source: github.language · Aug 2, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 2, 2026)

pip install deepchecks -U --user

Source link

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 deepchecks with "deepchecks[nlp]", and optionally install alsodeepchecks[nlp-properties]
For Computer Vision: Replace deepchecks with "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.

For agents

This page has a .md twin and JSON over the API.

/tools/deepchecks-deepchecks.md GET /api/graphcanon/tools/deepchecks-deepchecks GET /api/graphcanon/graph?tool=deepchecks-deepchecks