{"id":18863432,"url":"https://github.com/dem108/amlworkshop-iotedge-devops","last_synced_at":"2025-04-14T13:06:46.251Z","repository":{"id":89682071,"uuid":"185810513","full_name":"dem108/AMLWorkshop-IotEdge-DevOps","owner":"dem108","description":"This repo has some proposed agenda for Azure Machine Learning related hands-on workshops. 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full instructions [here](https://github.com/dem108/AMLWorkshop-IotEdge-DevOps/blob/master/agendas/many-models/Day1-automl-pipeline-basic.md)\n\n- Pre-requisites\n  - Python skills\n  - Understanding of [key concepts of Azure ML](https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture)\n  - Get familiar with Azure ML by running other experiments, trying own datasets, extending from previous workshops\n  - Bring any questions on overall Azure ML, share your feedbacks so far before Day 1\n\n- Morning\n  - Intro to agenda, getting to know each other\n  - Fundamentals\n  - Discussions\n\n- Afternoon\n  - Automated ML\n  - ML Pipeline\n  - Discussions\n\n### Day 2: Dive into Many Models - full instructions [here](https://github.com/dem108/AMLWorkshop-IotEdge-DevOps/blob/master/agendas/many-models/Day2-dive-into-many-models.md)\n\n- Pre-requisites\n  - Full understanding of key concepts on [ML Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines)\n  - Watch videos ([1](https://channel9.msdn.com/Shows/Docs-AI/Building-Large-Scale-Machine-Learning-Forecasting-Models-using-Azure-Machine-Learnings-Automated-ML), [2](https://channel9.msdn.com/Shows/Docs-AI/Building-Large-Scale-Machine-Learning-Models-using-Azure-Machine-Learning), less than 30 min in total) on the solution accelerator for many models\n  - Bring any questions on Pipeline before Day 2\n  - (Optional) Create SSH key pairs (OpenSSH rsa format) and install [VSCode](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-vs-code-remote) for remote connection\n\n- Morning\n  - Intro to [SA for many models](https://github.com/microsoft/solution-accelerator-many-models)\n  - Set up dev env (new compute instance, conda env, optional: vscode remote connection)\n  - Recap [ParallelRunStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps?preserve-view=true\u0026view=azure-ml-py)\n  - Discussions\n\n- Afternoon\n  - Follow [path 1: AutoML for Many Models](https://github.com/microsoft/solution-accelerator-many-models#using-automated-ml-to-train-the-models) (for both training and inferencing)\n  - (Optional) Follow [path 2: Custom ML models for Many Models](https://github.com/microsoft/solution-accelerator-many-models#using-a-custom-training-script-to-train-the-models)\n  - Discussions\n\n---\n\n## Agenda: ML/IoT/DevOps Hands on Workshop\n\n### Day 1: Azure ML Basic - full instructions [here](https://github.com/dem108/AMLWorkshop-IotEdge-DevOps/blob/master/agendas/mlops-edge/Day1-AzureML.md)\n\n- Common\n  - 09:30-10:00 Workshop overview, scope, expectations\n\n- ML Track\n  - 10:00-10:50 Dev environment setup: Azure ML service Workspace and Azure Notebooks. Authenticate, prepare compute (Azure ML Compute)\n  - 11:00-11:50 Train first DL model on Azure Notebooks using Azure ML Compute\n  - 13:00-14:50 Distributed training with Horovod on AML Compute, explore AML Workspace\n  - 15:00-16:50 Create container images, deploy to Azure Container Instance (and/or Azure Kubernetes Service)\n  - 17:00-17:50 Questions and answers\n\n### Day 1 (halfday version): Azure ML Basic - full instructions [here](https://github.com/dem108/AMLWorkshop-IotEdge-DevOps/blob/master/agendas/mlops-edge/Day1-AzureML-halfday.md)\n\n- Prepare (before workshop)\n  - Check Azure subscriptoin\n  - Install\n  \n- Afternoon\n  - 14:00-14:50 Workshop overview, scope, expectations and getting started\n  - 15:00-15:50 15:00-15:50 Visit AML studio, create computes and try Notebooks\n  - 16:00-16:50 16:00-16:50 Try Automated ML\n  - 17:00-17:50 17:00-17:50 Check out Designer and MLOps\n\n### Day 2: IoT and Edge Basic\n\n- IoT Track\n  - 09:30-10:00 Dev environment setup, Azure Resource creation (IoT Hub, DPS, Cosmos DB, ASA, Storage, etc)\n  - 10:00-10:30 Set-up Raspberry Pi\n  - 10:40-11:00 Run D2C message application on Pi\n  - 11:00-11:50 Provision a device using Azure IoT DPS (X.509 Individual Enrollment)\n  - 13:00-13:50 D2C message, Azure Stream Analytics, Data to Storage/DB\n  - 14:00-17:50 Custom Vision Edge module deployment\n\n### Day 3: ML + IoT Edge + DevOps - full instructions [here](https://github.com/dem108/AMLWorkshop-IotEdge-DevOps/blob/master/agendas/mlops-edge/Day3-DevOps-ML-IotEdge.md)\n\n- ML+IoT Edge+DevOps Track\n  - 09:30-10:00 Day 1, 2 reflection, Day 3 expectations\n  - 10:00-11:50 Dev environment setup: Use GitHub Desktop, Azure DevOps(create DevOps account, Organization), create from Azure ML template, customize Build Pipeline\n  - 13:00-14:50 Customize Release Pipeline, Git clone using personal token, test CI build\n  - 15:00-16:50 Integrate with IoT Edge deployment\n  - 17:00-17:50 Questions and answers\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdem108%2Famlworkshop-iotedge-devops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdem108%2Famlworkshop-iotedge-devops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdem108%2Famlworkshop-iotedge-devops/lists"}