Edge AI refers to artificial intelligence applied to edge devices like smartphones, tablets, laptops, cameras, sensors, and drones. It enables these devices to handle AI tasks autonomously, without cloud or central server connections, offering higher speed, lower latency, greater privacy, and reduced power consumption. Edge AI presents challenges and opportunities in model development and deployment, including size reduction, compression, quantization, and distillation, and involves integrating and communicating between edge devices and the cloud or other devices in a hybrid and distributed architecture. This tutorial provides practical guidance on developing and deploying optimized models for edge AI, covering theoretical and technical aspects, best practices, and real-world case studies focused on computer vision and deep learning models. We demonstrate tools and frameworks like TensorFlow, PyTorch, ONNX, OpenVINO, Google Mediapipe, and Qualcomm SNPE. We will also discuss multi-modal AI applications such as head pose estimation, person segmentation, hand gesture recognition, sound localization, and more. These applications use images, videos, and sounds to create interactive edge AI experiences. The presentation will include developing and deploying these models on Jabra collaborative business cameras and exploring integration with devices like Luxonis OAK-1 MAX, Neural Compute Engine Myriad X, and NVIDIA Jetson Nano Developer Kit.