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This repo has some proposed agenda for Azure Machine Learning related hands-on workshops.

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AMLWorkshop-IotEdge-DevOps

This repo caters different scenarios regarding Azure ML workshops


Agenda: Many Models with Azure ML

Day 1: AutoML and Pipelines Basic - full instructions here

  • 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
  • Morning

    • Intro to agenda, getting to know each other
    • Fundamentals
    • Discussions
  • Afternoon

    • Automated ML
    • ML Pipeline
    • Discussions

Day 2: Dive into Many Models - full instructions here


Agenda: ML/IoT/DevOps Hands on Workshop

Day 1: Azure ML Basic - full instructions here

  • Common

    • 09:30-10:00 Workshop overview, scope, expectations
  • 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

  • Prepare (before workshop)

    • Check Azure subscriptoin
    • Install
  • 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

Day 2: IoT and Edge Basic

  • 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

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