Eyes on AI - Computer Vision Engineering

Computer vision powers applications such as autonomous vehicles, smart retail, medical imaging, and industrial automation. In this Professional Certificate, you'll learn how to build, optimize, evaluate, and deploy computer vision systems used in real-world AI products . You’ll begin by preparing and analyzing vision datasets, applying augmentation techniques, and evaluating model performance using task-specific metrics and error analysis. You’ll also learn how to diagnose deep learning training issues and reproduce AI experiments using structured workflows. Next, you’ll optimize machine learning pipelines using PyTorch and modern MLOps practices. You’ll analyze GPU performance bottlenecks, design efficient data pipelines, visualize experiment results, and prepare models for deployment on edge devices. In the final stage, you’ll work with core computer vision tasks, including image classification, object detection, and image segmentation . You’ll fine-tune pre-trained models, evaluate prediction calibration, analyze annotation quality, configure anchor boxes, and refine segmentation outputs. Through hands-on projects that mirror real engineering tasks, you’ll gain practical skills for developing and maintaining production-ready vision AI systems

• Optimize deep learning workflows using PyTorch, GPU performance analysis, and efficient data pipelines
• Diagnose model failures and improve accuracy using metrics, calibration, and experiment analysis
• Deploy optimized AI models to edge environments and production inference pipelines

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work