{"data":{"slug":"deepchecks-deepchecks","name":"deepchecks","tagline":"Tests for Continuous Validation of ML Models & Data","github_url":"https://github.com/deepchecks/deepchecks","owner":"deepchecks","repo":"deepchecks","owner_avatar_url":"https://avatars.githubusercontent.com/u/92298186?v=4","primary_language":"Python","stars":4055,"forks":303,"topics":["data-drift","data-science","data-validation","deep-learning","html-report","jupyter-notebook","machine-learning","ml","mlops","model-monitoring","model-validation","pandas-dataframe","python","pytorch"],"archived":false,"github_pushed_at":"2025-12-28T12:07:44+00:00","maintenance_label":"Slowing","stars_delta_30d":14,"url":"https://www.graphcanon.com/tools/deepchecks-deepchecks","markdown_url":"https://www.graphcanon.com/tools/deepchecks-deepchecks.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/deepchecks-deepchecks","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=deepchecks-deepchecks","description":"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.","homepage_url":"https://docs.deepchecks.com/stable","license":"Other","open_issues":267,"watchers":24,"ai_summary":"Deepchecks is an open-source solution for validating AI and ML models and data, offering comprehensive testing from research to production.","readme_excerpt":"## ⏩  Getting Started\n\n<details close>\n   <summary>\n      <h3>\n         💻 Installation\n      </h3>\n   </summary>\n\n#### Deepchecks Testing (and CI) Installation\n\n```bash\npip install deepchecks -U --user\n```\n\nFor installing the nlp / vision submodules or with conda:\n- For NLP: Replace ``deepchecks`` with ``\"deepchecks[nlp]\"``, \n  and optionally install also``deepchecks[nlp-properties]``\n- For Computer Vision: Replace ``deepchecks`` with ``\"deepchecks[vision]\"``. \n- For installing with conda, similarly use: ``conda install -c conda-forge deepchecks``.\n\nCheck out the full installation instructions for deepchecks testing [here](https://docs.deepchecks.com/stable/getting-started/installation.html).\n\n#### Deepchecks Monitoring Installation\n\nTo 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.\nCreate a new directory for the installation files, open a terminal within that directory and run the following:\n\n```\npip install deepchecks-installer\ndeepchecks-installer install-monitoring\n```\n\nThis will automatically download the necessary dependencies, run the installation process\nand then start the application locally.\n\nThe installation will take a few minutes. Then you can open the deployment url (default is http://localhost),\nand start the system onboarding. Check out the full monitoring [open source installation & quickstart](https://docs.deepchecks.com/monitoring/stable/getting-started/deploy_self_host_open_source.html).\n\nNote that the open source product is built such that each deployment supports monitoring of\na single model.\n\n</details>","github_created_at":"2021-10-11T14:48:38+00:00","created_at":"2026-07-11T23:14:29.785893+00:00","updated_at":"2026-09-20T04:30:15.769654+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"data-drift","name":"data-drift"},{"slug":"data-science","name":"data-science"},{"slug":"data-validation","name":"data-validation"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"html-report","name":"html-report"},{"slug":"jupyter-notebook","name":"jupyter-notebook"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"ml","name":"ml"}],"trust":{"provenance":{"is_fork":false,"github_id":415969774,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:22:06.293Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":263,"last_release_at":"2024-12-15T15:25:39Z","stars_delta_30d":14,"open_issues_delta_30d":3},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:22:08.912Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:22:05.162Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-18T08:22:05.162Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-09-18T08:22:05.162Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to validate AI and ML models and data across different stages of the development lifecycle, from research to production.","If you are working with Python and want to integrate comprehensive testing and monitoring capabilities into your ML pipeline.","For teams that require detailed HTML reports and Jupyter notebook support for model validation and data drift detection."],"when_not_to_use":["If your project is not in Python or does not require integration with PyTorch or Pandas DataFrame.","When you need to monitor more than one model simultaneously with the open-source version, as it supports monitoring of a single model per deployment.","If you are looking for a tool that does not require manual installation steps for monitoring, as Deepchecks requires setting up a local instance or using their SaaS service."],"source":"enrich:decision_facts","observed_at":"2026-09-18T11:11:16.832Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Deepchecks is an open-source tool for continuous validation of ML models and data, offering comprehensive testing from research to production. It supports Python and integrates with popular frameworks like PyTorch."}]}}