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title AI on Edge Flagship Accelerator
description Empower your organization with production-ready Infrastructure as Code for Edge AI solutions. Achieve more with accelerated edge computing deployment using our comprehensive reusable components, blueprints, and default AI-assisted engineering practices.
author Edge AI Team
ms.date 2025-06-15
ms.topic hub-page
estimated_reading_time 2
variant primary
template splash
link {{REPO_URL}}
icon external
tagline Empower your organization with production-ready Infrastructure as Code for Edge AI solutions. Achieve more with accelerated edge computing deployment using our comprehensive suite of reusable components, deployment blueprints, and default AI-assisted engineering practices.
hero
text
View on GitHub
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keywords
edge AI
IoT operations
Kubernetes
infrastructure as code
Azure Arc
terraform
bicep

Build Status License: MIT Open in Dev Containers

What You'll Find Here

🎯 For Users

Ready to achieve rapid edge-ai deployments? Start with our General User Guide to deploy existing blueprints to Azure in 30-60 minutes.

🏗️ For Blueprint Developers

Creating new deployment scenarios? Start your process with the Blueprint Developer Guide to learn how to combine components into custom solutions that achieve your business goals.

⚙️ For Feature Developers

Contributing new capabilities? The Feature Developer Guide empowers you with component development, testing, and contribution workflows to achieve impactful contributions.

Key Features

  • Production-Ready: Battle-tested Infrastructure as Code that empowers organizations to achieve repeatedly deployable and reliable edge AI scenarios
  • Modular Design: Reusable components that enable teams to achieve custom solutions tailored to their unique business requirements
  • Multiple Frameworks: Support for both Terraform and Bicep for diverse technical requirements
  • AI-Assisted Development: Optimized for GitHub Copilot and AI-powered development workflows that accelerate team productivity
  • Comprehensive Testing: Automated validation and testing that ensures global-scale reliability for every deployment
  • Edge-Focused: Purpose-built capabilities that empower organizations worldwide to achieve edge AI computing workload success

🎓 Learning Platform

Empower your team to achieve proficiency in Edge-AI's AI-assisted, hyper-velocity engineering methodologies through challenge-based learning.

Learning provides hands-on training that empowers engineers to achieve expertise in edge-to-cloud AI systems with discovery-based coaching:

Learning Formats

  • 🥋 Katas: Focused practice exercises for skill building (15-45 minutes)
  • 🧪 Training Labs: Comprehensive hands-on experiences (2-8 hours)
  • 🤖 AI Coaching: Built-in VS Code coaching prompts for discovery-based learning

Ready to Start Learning?

🚀 Launch Documentation:

npm run docs

⏱️ Build Time:

  • First run: 2-4 minutes (installs dependencies + builds config)
  • Subsequent runs: ~30 seconds startup

Opens the complete documentation including the interactive Learning tab.

Repository Overview

How to Use This Repository

flowchart TD
    Start[I want to implement<br/>Edge AI solutions]

    %% User approach choices
    Quick[Quick Deploy<br/>Use existing blueprints]
    Custom[Custom Solution<br/>Build with components]
    Learn[Learn & Contribute<br/>Understand & extend]

  %% Learning paths
    Learning[learning/<br/>Learning Platform<br/>Paths & Katas]
    Katas[Individual Practice<br/>Katas 15-45 min]
    Labs[Team Exploration<br/>Labs 2-50+ hours]

    %% Repository structure navigation
    Blueprints[blueprints/<br/>Ready-to-deploy<br/>solutions]
    Components[src/<br/>Reusable<br/>components]
    Docs[docs/<br/>Documentation<br/>& guides]

    %% Common implementation scenarios
    PM[Predictive<br/>Maintenance]
    OPM[Performance<br/>Monitoring]
    QO[Process<br/>Optimization]

    %% Primary user flow
    Start --> Quick
    Start --> Custom
    Start --> Learn

    Quick --> Blueprints
    Custom --> Components
    Learn --> Docs
    Learn --> Learning
    Learning --> Katas
    Learning --> Labs

    %% Learning progression paths
    Katas --> Components
    Labs --> Blueprints
    Docs --> Components
    Components --> Blueprints

    %% Application to business scenarios
    Blueprints --> PM
    Blueprints --> OPM
    Blueprints --> QO

    %% Comprehensive color scheme for repository workflow
    style Start fill:#e1f5fe,stroke:#01579b,stroke-width:3px
    style Quick fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px
    style Custom fill:#fff3e0,stroke:#e65100,stroke-width:2px
    style Learn fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Learning fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Katas fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Labs fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Blueprints fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px
    style Components fill:#fff3e0,stroke:#e65100,stroke-width:2px
    style Docs fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style PM fill:#cffafe,stroke:#059669,stroke-width:2px
    style OPM fill:#cffafe,stroke:#059669,stroke-width:2px
    style QO fill:#cffafe,stroke:#059669,stroke-width:2px
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🎯 How Blueprints Build Edge Solutions

Blueprints are pre-configured compositions that combine Cloud Foundation, Edge Infrastructure, IoT Platform, and Observability components to deliver Industrial Automation, AI Workloads, and System Reliability.

🏗️ Nine Blueprint Types

  • Full Single Node → Complete solution with all components for comprehensive edge deployment
  • Full Multi Node → Enhanced distributed edge computing with load balancing and redundancy
  • Full Arc Multi Node → Hybrid cloud + edge with AKS and multiple edge nodes
  • Minimal Single Node → Core components only for resource-optimized deployment
  • Partial Single Node → Partially configured edge solution for specific use cases
  • Edge IoT Only → Add Azure IoT Operations to existing infrastructure
  • Cloud Only → Hosting-ready cloud infrastructure for edge workloads
  • CNCF Cluster Script → Automated deployment scripts for Kubernetes clusters
  • Fabric → Advanced analytics and data platform for edge-to-cloud insights

☁️ Cloud Foundation provides the supporting infrastructure

  • Resource Management: Resource groups, organization, governance
  • Security & Identity: Authentication, RBAC, Key Vault, certificates
  • Data Services: Data lakes, storage accounts, time-series databases
  • Messaging Services: Event Grid, Event Hubs, Service Bus

🖥️ Edge Infrastructure delivers the compute platform

  • VM Hosting: Virtual machines for edge hosting and management
  • Kubernetes Cluster: K3s with Arc-enabled management and orchestration
  • Networking: VNets, security groups, private endpoints

🏭 IoT Platform enables industrial connectivity

  • MQTT Broker: Secure messaging and communication hub
  • Data Processing: Real-time stream processing and analytics
  • Protocols: Industrial protocol translation and device integration

🔧 Device Management handles asset connectivity

  • OPC UA Assets: Industrial device integration and asset modeling
  • Asset Discovery: Automatic detection and onboarding of devices

📊 Observability ensures system health

  • Cloud Monitoring: Application Insights, Log Analytics, dashboards
  • Edge Monitoring: Local monitoring, health checks, performance metrics

🔧 Analytics Platform provides advanced data capabilities

  • Real-time Analytics: Stream processing and live data analysis
  • AI/ML Services: Machine learning model deployment and inference
  • Business Intelligence: Reporting, dashboards, and data visualization

🔗 Integration Services enable data flow and connectivity

  • Data Pipelines: ETL/ELT processes and data transformation
  • Event Streaming: Real-time event processing and routing
  • API Management: Service exposure and integration management

⚙️ Automation Tools streamline deployment and management

  • Deployment Scripts: Automated infrastructure provisioning
  • Configuration Management: Consistent system configuration and updates

Quick Start

  1. Choose your path from our Getting Started Guides
  2. Set up your environment with our Dev Container
  3. Deploy a blueprint from our Blueprint Catalog
  4. Explore components in our Component Library

Note on Telemetry: If you wish to opt-out of sending telemetry data to Microsoft when deploying Azure resources with Terraform, you can set the environment variable ARM_DISABLE_TERRAFORM_PARTNER_ID=true before running any terraform commands.

Community and Support

Ready to get started? Head to our Getting Started Guides and choose the path that matches your role!

Responsible AI

Microsoft encourages customers to review its Responsible AI Standard when developing AI-enabled systems to ensure ethical, safe, and inclusive AI practices. Learn more at Microsoft's Responsible AI.

📄 Legal

This project is licensed under the MIT License.

Security: See SECURITY.md for security policy and reporting vulnerabilities.

Trademark Notice

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

🤖 Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.