Edge AI

DeploymentIntermediate

Definition

Running AI models directly on local devices (smartphones, IoT devices, edge servers) rather than in the cloud. Offers benefits like lower latency, better privacy, offline capability, and reduced bandwidth costs. Requires optimized models through quantization and distillation.

Why "Edge AI" Matters in AI

Understanding edge ai is essential for anyone working with artificial intelligence tools and technologies. This deployment concept is critical for teams bringing AI models from development to production environments. Whether you're a developer, business leader, or AI enthusiast, grasping this concept will help you make better decisions when selecting and using AI tools.

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Related terms

Model DistillationQuantizationInferenceLatency

Frequently Asked Questions

What is Edge AI?

Running AI models directly on local devices (smartphones, IoT devices, edge servers) rather than in the cloud. Offers benefits like lower latency, better privacy, offline capability, and reduced bandw...

Why is Edge AI important in AI?

Edge AI is a intermediate concept in the deployment domain. Understanding it helps practitioners and users work more effectively with AI systems, make informed tool choices, and stay current with industry developments.

How can I learn more about Edge AI?

Start with our AI Fundamentals course, explore related terms in our glossary, and stay updated with the latest developments in our AI News section.

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