The basic study of system design is the understanding of component parts and their subsequent interaction with one another.
For example, services like Google, Twitter, Facebook, Amazon, and Netflix exemplify large-scale distributed systems.
ML systems require careful consideration of data pipelines, model training, and deployment infrastructure.
ML systems are often used in applications such as recommendation engines, fraud detection, and natural language processing.
The discipline overlaps with MLOps, a set of practices that unifies machine learning development and operations to ensure smooth deployment and lifecycle management of ML systems.