Azure Computer Vision

Computer vision is a field of artificial intelligence that enables machines to "see" and interpret visual information from images, videos, and live camera feeds, essentially giving computers the ability to analyze the world through digital eyes. Using specialized algorithms, computer vision systems can identify objects, detect faces, read text in photos, track motion, and understand scenes with increasing accuracy.

At its core, computer vision works by processing pixel data through layers of analysis,first detecting edges and shapes, then recognizing patterns, and finally classifying entire objects or scenes. Deep learning, particularly convolutional neural networks (CNNs), has dramatically improved computer vision accuracy by automatically learning hierarchical features from millions of example images. Common tasks include image classification (labeling what's in a picture), object detection (finding and locating multiple items), semantic segmentation (labeling each pixel by category), and optical character recognition (extracting text from images).

For the AI-900 certification exam, students must demonstrate understanding of computer vision workloads on Azure, specifically how to identify appropriate use cases and describe key capabilities of the vision services in AI Foundry Tools. The exam tests conceptual knowledge of features like image analysis, face detection, and OCR, focusing on when to use pre-built APIs versus custom models.