AMLWorkshop-IotEdge-DevOps

This repo caters different scenarios regarding Azure ML workshops

• Agenda: Many Models with Azure ML
• Agenda: ML/IoT/DevOps Hands on Workshop

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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

• 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

• Morning

  • Intro to SA for many models
  • Set up dev env (new compute instance, conda env, optional: vscode remote connection)
  • Recap ParallelRunStep
  • Discussions

• Afternoon

  • Follow path 1: AutoML for Many Models (for both training and inferencing)
  • (Optional) Follow path 2: Custom ML models for Many Models
  • Discussions

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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