{"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":4041,"forks":301,"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","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":264,"watchers":24,"ai_summary":"Deepchecks is an open-source solution for validation needs at all stages of AI projects 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":"2026-08-02T06:00:39.245274+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"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"data-drift","name":"data-drift"},{"slug":"ml","name":"ml"},{"slug":"mlops","name":"mlops"},{"slug":"model-monitoring","name":"model-monitoring"},{"slug":"pandas-dataframe","name":"pandas-dataframe"},{"slug":"pytorch","name":"pytorch"}],"trust":{"provenance":{"is_fork":false,"github_id":415969774,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-02T06:00:38.498Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":216,"last_release_at":"2024-12-15T15:25:39Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:14:31.652Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-02T06:00:38.943Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-02T06:00:38.943Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-08-02T06:00:38.943Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["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"],"when_not_to_use":["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"],"source":"enrich:decision_facts","observed_at":"2026-07-17T02:07:32.096Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"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."}]}}