Machine learning has transformed every major software product — search, recommendations, ads, fraud detection, content moderation — but the model itself is only a small piece of the puzzle. The real engineering challenge is everything around the model: the data pipelines that feed it, the training infrastructure that produces it, the serving systems that deliver predictions at scale, and the monitoring that catches silent failures. This course teaches the systems engineering behind production ML.

This is not an ML theory course. You will not derive loss functions or tune hyperparameters. Instead, you will learn how feature stores prevent training-serving skew, how distributed training coordinates hundreds of GPUs, how model servers handle thousands of inference requests per second, how A/B testing validates model improvements, and how monitoring detects model drift before it impacts users. These are the systems that make ML work in the real world.

The target audience is software engineers who want to understand ML infrastructure — whether you are building ML systems, designing platforms for ML teams, or preparing for system design interviews that involve ML components. By the end of this course, you will have the mental models to design, evaluate, and reason about production ML systems end to end.