Overview

Bridge the gap between data science and production by learning to deploy, scale, and maintain machine learning systems in real-world environments. You will build ML pipelines, implement model monitoring, manage feature stores, and optimize inference latency using cloud platforms and containerization. Projects include deploying recommendation APIs and building retraining workflows. This course is for software engineers and data professionals who want to specialize in the engineering discipline that makes AI reliable, fast, and maintainable at scale.