{"id":13289095,"url":"https://github.com/MG-Microsoft/MLOps_Workshop","last_synced_at":"2025-03-10T06:34:03.350Z","repository":{"id":42624417,"uuid":"369173039","full_name":"MG-Microsoft/MLOps_Workshop","owner":"MG-Microsoft","description":"Azure MLOps","archived":false,"fork":false,"pushed_at":"2024-02-21T13:07:42.000Z","size":255,"stargazers_count":105,"open_issues_count":0,"forks_count":248,"subscribers_count":5,"default_branch":"main","last_synced_at":"2024-07-29T17:04:48.351Z","etag":null,"topics":["azure","azure-devops","azure-machine-learning","devops","machine-learning","mlops","mlops-workflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MG-Microsoft.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-05-20T10:43:37.000Z","updated_at":"2024-07-04T11:04:12.000Z","dependencies_parsed_at":"2024-10-23T09:31:04.342Z","dependency_job_id":"12a3c4b0-9d55-42cd-9ead-91e2d1d0a0ad","html_url":"https://github.com/MG-Microsoft/MLOps_Workshop","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MG-Microsoft%2FMLOps_Workshop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MG-Microsoft%2FMLOps_Workshop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MG-Microsoft%2FMLOps_Workshop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MG-Microsoft%2FMLOps_Workshop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MG-Microsoft","download_url":"https://codeload.github.com/MG-Microsoft/MLOps_Workshop/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242805698,"owners_count":20187996,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["azure","azure-devops","azure-machine-learning","devops","machine-learning","mlops","mlops-workflow"],"created_at":"2024-07-29T17:00:25.774Z","updated_at":"2025-03-10T06:34:02.802Z","avatar_url":"https://github.com/MG-Microsoft.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Software License](https://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat-square)](LICENSE)\n\n# DevOps For Machine Learning | MLOps\nThis repository is created by [Mohammad Ghodratigohar]( https://www.linkedin.com/in/mohammad-ghodratigohar/) for hands-on MLOps workshop using [Azure Machine Learning]( https://docs.microsoft.com/en-us/azure/machine-learning/) and [Azure DevOps]( https://docs.microsoft.com/en-us/azure/devops/?view=azure-devops\u0026viewFallbackFrom=vsts). \n\nComplete implementation and explanation of this repository is recorded in these 10 part tutorial video series:\n[Video Series Playlist](https://www.youtube.com/playlist?list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f)\n\n[Part1](https://www.youtube.com/watch?v=-QxwB7PoSdA\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=3),\n[Part2](https://www.youtube.com/watch?v=Gzjr716RU9g\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=3),\n[Part3](https://www.youtube.com/watch?v=L-nIreup0HQ\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=1),\n[Part4](https://www.youtube.com/watch?v=b15l4BLAnmc\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=5),\n[Part5](https://www.youtube.com/watch?v=C79hIHRBSsQ\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=5),\n[Part6](https://www.youtube.com/watch?v=rPowmr43kzc\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=6),\n[Part7](https://www.youtube.com/watch?v=iq4hGqC_JMs\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=7),\n[Part8](https://www.youtube.com/watch?v=p9CxWhpE4uQ\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=8),\n[Part9](https://www.youtube.com/watch?v=y9NMFLBo3bQ\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=9),\n[Part10](https://www.youtube.com/watch?v=KHD2oyP8W94\u0026list=PLiQS6N-W1p3m9squzZ2cPgGdH5SBhjY6f\u0026index=10)\n\n\nFor any further inquiries or questions, please contact me at mo.ghodrati95@gmail.com .\n\n![ML Loop](./architecture/ml-loop.PNG)\n\n##  MLOps Workflow\n\nMachine Learning Operations ([MLOps]( https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment)) is based on DevOps principles and practices that increase the efficiency of workflows. \n\nThis repository contains codes and guidelines for configuring the MLOps workflow with Azure as shown below:\n\n![Flow](./architecture/flow.PNG)\n\n##  MLOps with Azure Machine Learning \n\nAzure Machine Learning provides the following MLOps capabilities:\n\n- **Machine Learning pipelines** allow you to define repeatable and reusable steps for your data preparation, training, and scoring processes.\n- **Create reusable software environments** for training and deploying models.\n- **Register, package, and deploy models** from anywhere. You can also track associated metadata required to use the model.\n- **Capture the governance data** for the end-to-end ML lifecycle. The logged information can include who is publishing models, why changes were made, and when models were deployed or used in production.\n- **Notify and alert on events in the ML lifecycle**. For example, experiment completion, model registration, model deployment, and data drift detection.\n- **Monitor ML applications for operational and ML-related issues**. Compare model inputs between training and inference, explore model-specific metrics, and provide monitoring and alerts on your ML infrastructure.\n- **Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines**. Using pipelines allows you to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services.\n\n![ML Lifecycle](./architecture/ml-lifecycle.png)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMG-Microsoft%2FMLOps_Workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMG-Microsoft%2FMLOps_Workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMG-Microsoft%2FMLOps_Workshop/lists"}