AI for Intelligent Cloud and Intelligent Edge: Discover, Deploy, and Manage with Azure ML Services
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The document covers the essentials of Azure Machine Learning, focusing on inferencing, model management, and deployment strategies for AI models. It outlines the AI lifecycle, the Azure ML deployment pipeline, and the differences between intelligent edge and cloud computing. Key components include high-speed data processing, real-time capabilities, and the challenges of running AI on edge devices.
AI for Intelligent Cloud and Intelligent Edge: Discover, Deploy, and Manage with Azure ML Services
- 2. Intro to Azure Machine Learning Inferencing Manage and deploy AI models
- 3. Building your own AI models for Transforming Data into Intelligence Prepare Data Build & Train Deploy
- 4. Azure AI Services Azure Infrastructure Tools
- 5. the team workspace - logical
- 6. Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy
- 7. 1/16/2019 9
- 8. My Computer Data Store Azure ML Workspace Compute Target Docker Image
- 9. 1/16/2019 11
- 12. http://notebooks.azure.com/ Import sample notebooks
- 14. Model Management
- 15. Docker Containers Azure Kubernetes Service (AKS) Azure Batch Azure IoT EdgeAzure Machine Learning Step 3: Deploy Any other container host…
- 16. Deploying models at scale 1/15/2019 18
- 17. The AI lifecycle 1/15/2019 19
- 18. The Azure ML Deployment Pipeline
- 19. 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
- 24. 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.
- 25. The Azure ML Deployment Pipeline
- 26. Challenges of Running AI on the Edge • Reduced Compute Power • No common HW abstraction for NN • Driver version fragmentation • Need familiarity with every platform
- 27. The components of a ML application Vision AI dev kit Vision AI dev kit
- 28. The components of a ML application
- 29. Vision AI Developer Kit Hardware Specification
- 30. Vision AI Development kit – System Architecture