This course helps you break down complex ML systems into clear, reusable parts and communicate them using practical abstractions. You’ll learn how to separate ingestion, feature serving, inference APIs, and monitoring components while creating flowcharts and pseudocode that guide implementation. Using examples such as real-time fraud detection and feature store workflows, you’ll practice decomposing systems and designing abstractions that engineers depend on. Through short videos, readings, hands-on practice, a coach-guided reflection, and a 45-minute ungraded lab, you’ll build skills used across ML engineering and MLOps roles. By the end, you’ll be able to confidently analyze ML systems and produce artifacts that support scaling, clarity, and production readiness.