{"data":{"slug":"visenger-awesome-mlops","name":"awesome-mlops","tagline":"A curated list of references for MLOps","github_url":"https://github.com/visenger/awesome-mlops","owner":"visenger","repo":"awesome-mlops","owner_avatar_url":"https://avatars.githubusercontent.com/u/2014749?v=4","primary_language":null,"stars":13952,"forks":2072,"topics":["ai","data-science","devops","engineering","federated-learning","machine-learning","ml","mlops","software-engineering"],"archived":false,"github_pushed_at":"2024-11-21T14:45:11+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/visenger-awesome-mlops","markdown_url":"https://www.graphcanon.com/tools/visenger-awesome-mlops.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/visenger-awesome-mlops","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=visenger-awesome-mlops","description":"A curated list of references for MLOps ","homepage_url":"https://ml-ops.org","license":null,"open_issues":42,"watchers":382,"ai_summary":null,"readme_excerpt":"# MLOps: Model Deployment and Serving\n<details>\n<summary>Click to expand!</summary>\n \n1. [AI Infrastructure for Everyone: DeterminedAI](https://determined.ai/)\n1. [Deploying R Models with MLflow and Docker](https://mdneuzerling.com/post/deploying-r-models-with-mlflow-and-docker/)\n1. [What Does it Mean to Deploy a Machine Learning Model?](https://mlinproduction.com/what-does-it-mean-to-deploy-a-machine-learning-model-deployment-series-01/)\n1. [Software Interfaces for Machine Learning Deployment](https://mlinproduction.com/software-interfaces-for-machine-learning-deployment-deployment-series-02/)\n1. [Batch Inference for Machine Learning Deployment](https://mlinproduction.com/batch-inference-for-machine-learning-deployment-deployment-series-03/)\n1. [AWS Cost Optimization for ML Infrastructure - EC2 spend](https://blog.floydhub.com/aws-cost-optimization-for-ml-infra-ec2/)\n1. [CI/CD for Machine Learning & AI](https://blog.paperspace.com/ci-cd-for-machine-learning-ai/)\n1. [Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with ***online training*** in Kubeflow](https://cloud.google.com/blog/products/ai-machine-learning/itau-unibanco-how-we-built-a-cicd-pipeline-for-machine-learning-with-online-training-in-kubeflow)\n1. [101 For Serving ML Models](https://pakodas.substack.com/p/101-for-serving-ml-models-10217c9f0764)\n1. [Deploying Machine Learning models to production — **Inference service architecture patterns**](https://medium.com/data-for-ai/deploying-machine-learning-models-to-production-inference-service-architecture-patterns-bc8051f70080)\n1. [Serverless ML: Deploying Lightweight Models at Scale](https://mark.douthwaite.io/serverless-machine-learning/)\n1. ML Model Rollout To Production. [Part 1](https://www.superwise.ai/resources-old/safely-rolling-out-ml-models-to-production) | [Part 2](https://www.superwise.ai/blog/part-ii-safely-rolling-out-models-to-production)\n1. [Deploying Python ML Models with Flask, Docker and Kubernetes](https://alexioannides.com/2019/01/10/deploying-python-ml-models-with-flask-docker-and-kubernetes/)\n1. [Deploying Python ML Models with Bodywork](https://alexioannides.com/2020/12/01/deploying-ml-models-with-bodywork/)\n1. [Framework for a successful Continuous Training Strategy. When should the model be retrained? What data should be used? What should be retrained? A data-driven approach](https://towardsdatascience.com/framework-for-a-successful-continuous-training-strategy-8c83d17bb9dc)\n1. [Efficient Machine Learning Inference. The benefits of multi-model serving where latency matters](https://www.oreilly.com/content/efficient-machine-learning-inference/)\n1. [Deploying Hugging Face ML Models in the Cloud with Infrastructure as Code](https://www.pulumi.com/blog/mlops-the-ai-challenge-is-cloud-not-code/)\n</details>\n\n \n<a name=\"testing-monintoring\"></a>\n\n---\n\n# MLOps: Infrastructure & Tooling\n<details>\n<summary>Click to expand!</summary>\n \n1. [MLOps Infrastructure Stack Canvas](https://miro.com/app/board/o9J_lfoc4Hg=/)\n1. [Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps](https://towardsdatascience.com/rise-of-the-canonical-stack-in-machine-learning-724e7d2faa75)\n1. [AI Infrastructure Alliance. Building the canonical stack for AI/ML](https://ai-infrastructure.org/)\n1. [Linux Foundation AI Foundation](https://wiki.lfai.foundation/)\n1. ML Infrastructure Tools for Production | [Part 1 — Production ML — The Final Stage of the Model Workflow](https://towardsdatascience.com/ml-infrastructure-tools-for-production-1b1871eecafb) | [Part 2 — Model Deployment and Serving](https://towardsdatascience.com/ml-infrastructure-tools-for-production-part-2-model-deployment-and-serving-fcfc75c4a362)\n1. [The MLOps Stack Template (by valohai)](https://valohai.com/blog/the-mlops-stack/)\n1. [Navigating the MLOps tooling landscape](https://ljvmiranda921.github.io/notebook/2021/05/10/navigating-the-mlops-landscape/)\n1. [MLOps.toy","github_created_at":"2020-03-03T11:37:19+00:00","created_at":"2026-07-11T23:39:26.779723+00:00","updated_at":"2026-07-11T23:39:30.339809+00:00","categories":[{"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"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"},{"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":"engineering","name":"engineering"},{"slug":"data-science","name":"data-science"},{"slug":"ml","name":"ml"},{"slug":"ai","name":"ai"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"federated-learning","name":"federated-learning"},{"slug":"mlops","name":"mlops"},{"slug":"devops","name":"devops"}],"trust":{"provenance":{"is_fork":false,"github_id":244620269,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:39:28.048Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":597,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:39:28.493Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T23:39:27.808Z"}}}}