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AI for Intelligent Cloud and Intelligent Edge: Discover, Deploy, and Manage with Azure ML Services

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AI for Intelligent Cloud and Intelligent Edge: Discover, Deploy, and Manage with Azure ML Services

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This is a hack material to help IoT partners/customers to know how to build a ML Edge module with Azure ML Service and deploy it to the Edge device.

This is a hack material to help IoT partners/customers to know how to build a ML Edge module with Azure ML Service and deploy it to the Edge device.

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AI for Intelligent Cloud and Intelligent Edge: Discover, Deploy, and Manage with Azure ML Services

  1. 1. Intro to Azure Machine Learning Inferencing Manage and deploy AI models
  2. 2. Building your own AI models for Transforming Data into Intelligence Prepare Data Build & Train Deploy
  3. 3. Azure AI Services Azure Infrastructure Tools
  4. 4. the team workspace - logical
  5. 5. Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy
  6. 6. 1/16/2019 9
  7. 7. My Computer Data Store Azure ML Workspace Compute Target Docker Image
  8. 8. 1/16/2019 11
  9. 9. http://portal.azure.com
  10. 10. http://notebooks.azure.com/ Import sample notebooks
  11. 11. Model Management
  12. 12. Docker Containers Azure Kubernetes Service (AKS) Azure Batch Azure IoT EdgeAzure Machine Learning Step 3: Deploy Any other container host…
  13. 13. Deploying models at scale 1/15/2019 18
  14. 14. The AI lifecycle 1/15/2019 19
  15. 15. The Azure ML Deployment Pipeline
  16. 16. Understanding the Edge: Heavy Edge vs Light Edge Cloud: Azure Heavy Edge Light Edge Description An Azure host that spans from CPU to GPU and FPGA VMs A server with slots to insert CPUs, GPUs, and FPGAs or a X64 or ARM system that needs to be plugged in to work A Sensor with a SOC (ARM CPU, NNA, MCU) and memory that can operate on batteries Example DSVM / ACI / AKS / Batch AI - DataBox Edge - HPE - Azure Stack - DataBox Edge - Industrial PC -Video Gateway -DVR -Mobile Phones -VAIDK -Mobile Phones -IP Cameras -Azure Sphere - Appliances What runs model CPU,GPU or FPGA CPU,GPU or FPGA CPU, GPU x64 CPU Multi-ARM CPU Hw accelerated NNA CPU/GPU MCU
  17. 17. Model Management Edge Integration
  18. 18. Why Intelligent Edge? High-speed data processing, analytics and shorter response times are more essential than ever. Intelligent Cloud • Business agility and scalability: unlimited computing power available on demand. Intelligent Edge • Can handle priority-one tasks locally even without cloud connection. • Can handle generated data that is too large to pull rapidly from the cloud. • Enables real-time processing through intelligence in or near to local devices. • Flexibility to accommodate data privacy related requirements.
  19. 19. The Azure ML Deployment Pipeline
  20. 20. Challenges of Running AI on the Edge • Reduced Compute Power • No common HW abstraction for NN • Driver version fragmentation • Need familiarity with every platform
  21. 21. The components of a ML application Vision AI dev kit Vision AI dev kit
  22. 22. The components of a ML application
  23. 23. Vision AI Developer Kit Hardware Specification
  24. 24. Vision AI Development kit – System Architecture
  25. 25. https://.portal.azure.com https://docs.microsoft.com/zh-tw/azure/iot-edge/quickstart-linux https://docs.microsoft.com/zh-tw/azure/iot-edge/tutorial-deploy- machine-learning

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