What are the challenges and opportunities of deploying AI models on edge devices?

Deploying AI models on edge devices such as smartphones, IoT devices, and edge servers presents challenges related to limited computational resources, energy efficiency, and privacy concerns. However, edge AI offers opportunities for low-latency inference, real-time processing, and privacy-preserving data analysis without relying on cloud services. Techniques such as model compression, quantization, and federated learning are used to optimize AI models for edge deployment.