This specialization prepares you to build, evaluate, and deploy production-ready object detection and image segmentation systems. Across eight hands-on courses, you'll learn to create quality-controlled vision datasets, train and evaluate models using metrics like mAP, IoU, and Dice, and diagnose performance issues through slice-level analysis and error logging. You'll build real-time detection pipelines with YOLOv8 and DeepSORT, refine segmentation outputs using post-processing techniques like CRF smoothing, and optimize models for edge deployment with TensorFlow Lite. The program also covers deploying scalable inference workflows using Docker and AWS Lambda, calibrating confidence scores for trustworthy predictions, and communicating results to technical and non-technical stakeholders. By completion, you'll have the end-to-end skills to take computer vision models from notebooks to reliable, production-grade systems.
Applied Learning Project
Throughout this specialization, you will complete hands-on projects that mirror real-world computer vision workflows. You'll build a quality-controlled annotation pipeline using CVAT, compute anchor box parameters from clustered object measurements, and evaluate detection models against throughput requirements like 25 FPS at 720p. You'll also construct a segmentation refinement pipeline incorporating CRF-based smoothing and morphological operations, diagnose class imbalance issues using focal-dice hybrid loss strategies, and deploy a serverless batch-inference system on AWS Lambda. These projects give you practical experience solving authentic challenges faced by MLOps and computer vision teams in production environments.
