This course teaches you how to evaluate and optimize machine learning models for reliable performance on edge devices. You’ll learn how to move beyond overall accuracy by analyzing model behavior across meaningful data slices—such as device type or environmental conditions—to uncover hidden robustness and fairness issues. You’ll also explore how models are optimized for edge deployment using TensorFlow Lite, including how quantization affects model size, inference speed, and accuracy. Through videos, hands-on activities, and guided reflection, you’ll practice interpreting these trade-offs and communicating deployment readiness clearly. By the end of the course, you’ll be able to assess slice-level performance gaps, evaluate optimization outcomes, and make informed decisions about deploying models in real-world edge environments.