Model
What is Model?
A model in Edge AI is a trained algorithm or digital blueprint that enables devices to make intelligent decisions locally, without cloud dependence. Also called an AI model, it interprets data, predicts outcomes, and drives autonomous actions at the network edge.
A model is also a mathematical representation or algorithm trained on data to recognize patterns, make predictions, or automate decision-making directly on edge devices. It is the core intelligence that powers AI at the edge.
Why Is It Used?
Models are used to enable fast, real-time decision-making, reduce latency, conserve bandwidth, and ensure privacy by processing data locally rather than sending it to centralized servers.
How Is It Used?
Deployed on edge devices like IoT sensors, cameras, or industrial machines
Processes data locally in real time
Sends actionable insights or triggers automated responses without relying on cloud connectivity
Types of Model
Supervised Models: Learn from labeled datasets
Unsupervised Models: Discover patterns without predefined labels
Reinforcement Models: Learn through trial-and-error interactions
Lightweight Edge Models: Optimized for low-power devices and minimal computational load
Benefits of Model
Low Latency: Instant insights and actions
Bandwidth Efficiency: Reduces cloud communication
Enhanced Privacy: Data processed locally
Scalable Intelligence: Enables smarter IoT and industrial applications