Build & Evaluate Real-Time Object Detectors is an intermediate hands-on course for ML engineers who need to deploy fast, accurate object detectors under real-world constraints. When accuracy falls short of KPIs, or FPS drops below target, you need the skills to diagnose metrics, recommend improvements, and evaluate whether a real-time pipeline meets requirements. You'll learn how to compute and interpret detection metrics like mAP and APsmall, identify causes of underperformance, and propose targeted improvements. Then you'll analyze a complete real-time detection pipeline using models like YOLOv8 and trackers like DeepSORT, and evaluate it against throughput requirements such as 25 FPS at 720p. Through short videos, practical readings, analysis-based labs, and a final graded assessment, you will develop the skills to evaluate detectors, recommend optimizations, and assess whether solutions meet real-time demands.