We live in the era of data. With the Internet of Things (IoT) and Artificial Intelligence (AI) becoming ubiquitous technologies, we now have huge volumes of data being generated. Differing in form, data could be speech, text, image, or a mix of any of these. In the form of photos or videos, **images make up for a significant share of global data creation.** AIoT, the combination of AI and IoT, enables the development of highly scalable systems that leverage machine learning for distributed data analysis. ##### **The need for AI to understand image data** Since the vast amount of image data we obtain from cameras and sensors is **unstructured**, we depend on advanced techniques such as machine learning algorithms to analyze the images efficiently. Image classification is probably the most important part of digital image analysis. It uses AI-based deep learning models to analyze images with results that for specific tasks already surpass human-level accuracy (for example, in face recognition). Since AI is computationally very intensive and involves the transmission of huge amounts of potentially sensitive visual information, **processing image data in the cloud comes with severe limitations.** Therefore, there is a big emerging trend called **Edge AI** that aims to move machine learning (ML) tasks from the cloud to the edge. This allows moving ML computing close to the source of data, specifically to edge devices (computers) that are connected to cameras. Performing machine learning for image recognition at the edge makes it possible to overcome the limitations of the cloud in terms of privacy, real-time performance, efficacy, robustness, and more. Hence, the use of Edge AI for computer vision makes it possible to scale image recognition applications in real-world scenarios.   ##### Image Classification is the Basis of Computer Vision The field of computer vision includes a set of main problems such as image classification, localization, image segmentation, and object detection. Among those, image classification can be considered the **fundamental problem**. It forms the basis for other computer vision problems. Image classification applications are used in many areas, such as medical imaging, object identification in satellite images, traffic control systems, brake light detection, machine vision, and more.