{"id":21488864,"url":"https://github.com/d-kleine/az_ml-engineering","last_synced_at":"2026-05-11T16:37:30.166Z","repository":{"id":199999705,"uuid":"704167666","full_name":"d-kleine/AZ_ML-Engineering","owner":"d-kleine","description":"ML Engineering with MS Azure","archived":false,"fork":false,"pushed_at":"2024-02-26T13:08:50.000Z","size":19958,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-17T10:52:33.727Z","etag":null,"topics":["automl","azure","hyperdrive","ml-engineering","mlops"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Engineering with Microsoft Azure\n\n![Azure Logo](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Microsoft_Azure_Logo.svg/1280px-Microsoft_Azure_Logo.svg.png)\n\nDevelop a comprehensive understanding of machine learning models, data privacy safeguards, and effective end-to-end management of the machine learning lifecycle at scale using Azure Machine Learning's MLOps capabilities.\n\n## Program structure\n\n### Azure Machine Learning\n\n- Understanding the rationale for cloud-based machine learning.\n- Efficiently utilizing workspaces and AzureML Studio.\n- Integrating third-party and open datasets into machine learning pipelines.\n- Managing pipelines and leveraging hyperparameters for improved prediction accuracy.\n- Programmatically creating and managing pipelines using the Azure ML SDK.\n- Automating machine learning processes with Hyperparameter Tuning and AutoML.\n\n  → [Project: Optimizing an ML Pipeline](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project1_Optimizing-an-ML-Pipeline)\n\n### Operationalizing Machine Learning\n\n- Authorizing operations for machine learning.\n- Deploying machine learning models in Azure.\n- Consuming and load-testing deployed services and endpoints.\n- Creating batch inference pipelines and publishing them.\n- Applying DevOps concepts for model deployment.\n- Configuring and deploying a cloud-based machine learning production model using Azure.\n\n  → [Project: Operationalizing-ML (MLOps)](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project2_Operationalizing-ML)\n\n### Capstone project\n* Combining all skills acquired in this program for a self-choosen ML project\n  → [Capstone project: Heart Failure Prediction with AzureML](https://github.com/d-kleine/AZ_ML-Engineering/tree/main/project3_Capstone)\n\n## Skills\n\n- **Azure Machine Learning:** Azure ML platform, Azure ML pipelines, Model interpretation, Azure ML SDK, Hyperparameter tuning.\n- **Machine Learning Operations:** Model deployment with Azure, Kubernetes security, Deployment testing, Docker, Model evaluation.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd-kleine%2Faz_ml-engineering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fd-kleine%2Faz_ml-engineering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd-kleine%2Faz_ml-engineering/lists"}