Edge AI Introduction

What is Edge AI?

Edge AI brings artificial intelligence capabilities directly to devices at the edge of the network—phones, sensors, cameras, and IoT gateways—so they can process data locally, without relying on cloud connectivity.

Why Use Edge AI?

• Low latency: Instant decisions without round‑trip delays.
• Privacy: Data never leaves the device, reducing exposure.
• Bandwidth savings: Only relevant results are transmitted.
• Reliability: Works offline or with intermittent connectivity.

Key Components

Typical edge AI stacks consist of:

1. Hardware accelerators: GPUs, NPUs, or ASICs optimized for inference.
2. Runtime libraries: ONNX Runtime, TensorFlow Lite, or Microsoft Edge AI SDK.
3. Model optimization: Quantization, pruning, and model compression.
4. Deployment pipelines: CI/CD for seamless updates.

Real‑World Examples

Edge AI is already powering a variety of solutions:

• Smart cameras detecting anomalies in real time.
• Industrial robots performing quality inspections on the fly.
• Healthcare wearables analyzing vital signs locally.
• Autonomous drones navigating without cloud assistance.

Getting Started

To build your first edge AI app on Windows IoT:

# Install the Edge AI SDK
dotnet tool install -g Microsoft.EdgeAI

# Create a new project
edgeai new vision-app --model yolov5

# Build and deploy
edgeai build --device raspberrypi
edgeai deploy --device raspberrypi

For more detailed guides, visit the Edge AI Resources page.