Example Concept: Deploying AI models on edge devices involves optimizing models to run efficiently on hardware with limited resources. This often requires model compression techniques such as quantization and pruning, which reduce the model size and computational requirements without significantly sacrificing accuracy. Additionally, developers must consider the trade-offs between latency, power consumption, and performance to ensure the models operate effectively in real-time scenarios.