{"data":{"slug":"fuzzylabs-awesome-open-mlops","name":"awesome-open-mlops","tagline":"Model deployment and serving guide with open-source MLOps tools","github_url":"https://github.com/fuzzylabs/awesome-open-mlops","owner":"fuzzylabs","repo":"awesome-open-mlops","owner_avatar_url":"https://avatars.githubusercontent.com/u/46245244?v=4","primary_language":null,"stars":482,"forks":54,"topics":["datascience","devops","infrastructure","machine-learning","machinelearning","mlops"],"archived":false,"github_pushed_at":"2025-05-19T07:41:48+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/fuzzylabs-awesome-open-mlops","markdown_url":"https://www.graphcanon.com/tools/fuzzylabs-awesome-open-mlops.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fuzzylabs-awesome-open-mlops","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops","description":"The Fuzzy Labs guide to the universe of open source MLOps","homepage_url":null,"license":"Apache-2.0","open_issues":6,"watchers":10,"ai_summary":"A curated list of open-source MLOps projects focused on model deployment and serving.","readme_excerpt":"# Model deployment and serving\n\nModel serving is the process of taking a trained model and presenting it behind a REST API, and this enables other software components to interact with a model. To make deployment of these model servers as simple as possible, it's commonplace to run them inside Docker containers and deploy them to a container orchestration system such as Kubernetes.\n\n| Name                                                   | License    | Description                                              |\n| ------------------------------------------------------ | ---------- | -------------------------------------------------------- |\n| [BentoML](https://github.com/bentoml/BentoML)          | Apache 2.0 |                                                          |\n| [Bodywork](https://www.bodyworkml.com)                 | AGPL-3.0   |                                                          |\n| KServe                                                 | Apache 2.0 |                                                          |\n| [MLEM](https://github.com/iterative/mlem)              | Apache 2.0 | 🐶 Version and deploy your ML models following GitOps principles |","github_created_at":"2021-11-16T15:46:11+00:00","created_at":"2026-07-11T23:39:01.460825+00:00","updated_at":"2026-08-04T18:02:06.131281+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"datascience","name":"datascience"},{"slug":"devops","name":"devops"},{"slug":"infrastructure","name":"infrastructure"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"mlops","name":"mlops"}],"trust":{"provenance":{"is_fork":false,"github_id":428716017,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T18:02:05.090Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":442,"last_release_at":"2021-12-13T15:35:51Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:39:07.124Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T18:02:05.578Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-04T18:02:05.578Z"}},"decision_facts":{"hosting":{"model":"unknown","summary":"No specific details available."},"pricing":{"model":"freemium","summary":"`awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource."},"requirements":null,"constraints":{"hosting_model":"unknown","pricing_model":"freemium"},"when_to_use":["When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases","If your project requires Apache 2.0 licensed tools to ensure compatibility with other Apache-licensed software components"],"when_not_to_use":["Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects","Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required"],"source":"enrich:decision_facts","observed_at":"2026-07-17T05:12:00.407Z"},"constraint_facets":{"hosting_model":"unknown","pricing_model":"freemium"},"decision_summary":[{"label":"Hosting","value":"unknown - No specific details available."},{"label":"Pricing","value":"freemium - `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource."},{"label":"Adopt for","value":"awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs."},{"label":"License detail","value":"Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts."}]}}