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Azure IoT Edge - Edge Computing for IoT

Azure IoT Edge runs cloud workloads on IoT devices for local processing and offline operation.

hybrid-multicloud
Pricing Model Free runtime, pay for connected cloud services
Availability Global (cloud), any hardware (edge)
Data Sovereignty Data processed locally on edge devices
Reliability 99.9% for IoT Hub SLA

What is Azure IoT Edge?

Azure IoT Edge extends cloud computing to IoT devices at the network edge. It runs a container-based runtime on edge devices, enabling you to deploy cloud workloads (AI models, Azure Functions, custom logic) that execute locally. Devices can operate independently when disconnected and sync with Azure IoT Hub when connectivity is available.

This architecture reduces latency for time-sensitive decisions, lowers bandwidth costs by processing data locally, and enables operation in environments with intermittent connectivity.

Core Features

  • Container-based modules: Deploy Docker containers to edge devices
  • Cloud deployment: Manage edge workloads through Azure IoT Hub
  • Offline operation: Continue processing when disconnected from cloud
  • Built-in modules: Stream Analytics, Functions, and ML modules available
  • Security: Hardware root of trust and automatic certificate management

Typical Use Cases

IoT Edge is used in manufacturing for real-time quality inspection, in retail for inventory analytics, in transportation for vehicle diagnostics, and anywhere that requires local processing of sensor data. It is particularly valuable when latency, bandwidth, or connectivity constraints make cloud-only architectures impractical.

Benefits

  • Sub-second response times for local decisions
  • Reduced data transfer costs
  • Consistent development model for cloud and edge
  • Central management of distributed devices

Frequently Asked Questions

What hardware does IoT Edge support?

IoT Edge runs on Linux (x64, ARM32, ARM64) and Windows (x64) devices. Requirements are modest: 1 GB RAM and a container runtime. It runs on industrial PCs, gateways, and even Raspberry Pi for development.

How do I deploy machine learning models?

Train models in Azure Machine Learning, export as container images, and deploy to IoT Edge devices through IoT Hub. The ML module runs inference locally using the device’s CPU or GPU.

Can IoT Edge modules communicate with each other?

Yes. Modules on the same device can exchange messages through the IoT Edge Hub, enabling pipelines where one module’s output feeds another. This follows the same message routing patterns as cloud IoT Hub.

How does offline operation work?

IoT Edge Hub stores messages locally when the cloud connection is unavailable. When connectivity returns, messages are automatically synced to IoT Hub. You configure retention policies for how long and how much data to store.

Integration with innFactory

As a Microsoft Solutions Partner, innFactory helps you build IoT Edge solutions: architecture design, module development, and deployment automation for industrial and commercial IoT scenarios.

Typical Use Cases

Industrial automation
Predictive maintenance
Video analytics at the edge
Offline-capable IoT solutions

Microsoft Solutions Partner

innFactory is a Microsoft Solutions Partner. We provide expert consulting, implementation, and managed services for Azure.

Microsoft Solutions Partner Microsoft Data & AI

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