deepchecks/deepchecks
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. observed · 2026-10-12
Health v2 · maintenance only
57/100
- Activity 51
- Release rhythm 40
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 10
- age_days: 1835
- days_rel: 674
- days_push: 296
- n_releases_24m: 2
Adoption not part of the score
4060 stars · 300 forks observed · 2026-10-12
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Deepchecks is an open-source Python library for continuous validation of machine learning models and data, covering testing, CI integration, and monitoring from research to production. It provides built-in checks for data integrity, data drift, and model performance with HTML report output.
Use cases
- validate ml model quality before deployment
- detect data drift in production
- test training data for integrity issues
- add model validation to ci pipeline
- generate html reports of model evaluation
- monitor model performance over time
When to choose
- you need structured, reusable checks for ML data and models across research and production
- you want drift detection and model monitoring in a Python workflow with pandas or PyTorch
- you need shareable HTML validation reports for stakeholders
When to avoid
- you need a full hosted ML observability platform with dashboards and alerting out of the box
- your stack is not Python-based
- the AGPLv3 license is incompatible with your project's licensing
Facets
library · maturity active
testing monitoring machine-learning data-science benchmarking machine-learning data-science python cross-platform model-validation data-validation data-drift mlops model-monitoring html-report pytorch pandas jupyter-notebook agpl-license data-engineering
3 sources
- readme: https://github.com/deepchecks/deepchecks · fetched 2026-10-12 · bce0bc7072a1
- homepage: https://docs.deepchecks.com/stable · fetched 2026-08-29 · fe75457f84e2
- registry_pypi: https://pypi.org/pypi/deepchecks/json · fetched 2026-08-29 · d51b706cf9a3
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| deepchecks/deepchecks | main | 57 |
For agents
markdown · JSON · MCP: product_card(name="deepchecks/deepchecks")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem