This repo caters different scenarios regarding Azure ML workshops
Day 1: AutoML and Pipelines Basic - full instructions here
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Pre-requisites
- Python skills
- Understanding of key concepts of Azure ML
- Get familiar with Azure ML by running other experiments, trying own datasets, extending from previous workshops
- Bring any questions on overall Azure ML, share your feedbacks so far before Day 1
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Morning
- Intro to agenda, getting to know each other
- Fundamentals
- Discussions
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Afternoon
- Automated ML
- ML Pipeline
- Discussions
Day 2: Dive into Many Models - full instructions here
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Pre-requisites
- Full understanding of key concepts on ML Pipelines
- Watch videos (1, 2, less than 30 min in total) on the solution accelerator for many models
- Bring any questions on Pipeline before Day 2
- (Optional) Create SSH key pairs (OpenSSH rsa format) and install VSCode for remote connection
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Morning
- Intro to SA for many models
- Set up dev env (new compute instance, conda env, optional: vscode remote connection)
- Recap ParallelRunStep
- Discussions
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Afternoon
- Follow path 1: AutoML for Many Models (for both training and inferencing)
- (Optional) Follow path 2: Custom ML models for Many Models
- Discussions
Day 1: Azure ML Basic - full instructions here
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Common
- 09:30-10:00 Workshop overview, scope, expectations
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ML Track
- 10:00-10:50 Dev environment setup: Azure ML service Workspace and Azure Notebooks. Authenticate, prepare compute (Azure ML Compute)
- 11:00-11:50 Train first DL model on Azure Notebooks using Azure ML Compute
- 13:00-14:50 Distributed training with Horovod on AML Compute, explore AML Workspace
- 15:00-16:50 Create container images, deploy to Azure Container Instance (and/or Azure Kubernetes Service)
- 17:00-17:50 Questions and answers
Day 1 (halfday version): Azure ML Basic - full instructions here
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Prepare (before workshop)
- Check Azure subscriptoin
- Install
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Afternoon
- 14:00-14:50 Workshop overview, scope, expectations and getting started
- 15:00-15:50 15:00-15:50 Visit AML studio, create computes and try Notebooks
- 16:00-16:50 16:00-16:50 Try Automated ML
- 17:00-17:50 17:00-17:50 Check out Designer and MLOps
- IoT Track
- 09:30-10:00 Dev environment setup, Azure Resource creation (IoT Hub, DPS, Cosmos DB, ASA, Storage, etc)
- 10:00-10:30 Set-up Raspberry Pi
- 10:40-11:00 Run D2C message application on Pi
- 11:00-11:50 Provision a device using Azure IoT DPS (X.509 Individual Enrollment)
- 13:00-13:50 D2C message, Azure Stream Analytics, Data to Storage/DB
- 14:00-17:50 Custom Vision Edge module deployment
Day 3: ML + IoT Edge + DevOps - full instructions here
- ML+IoT Edge+DevOps Track
- 09:30-10:00 Day 1, 2 reflection, Day 3 expectations
- 10:00-11:50 Dev environment setup: Use GitHub Desktop, Azure DevOps(create DevOps account, Organization), create from Azure ML template, customize Build Pipeline
- 13:00-14:50 Customize Release Pipeline, Git clone using personal token, test CI build
- 15:00-16:50 Integrate with IoT Edge deployment
- 17:00-17:50 Questions and answers