What is ML system architecture in large-scale production environments?

Large-scale ML architecture includes data ingestion, feature pipelines, training systems, model registry, and scalable inference services.

A production-grade ML architecture is composed of multiple loosely coupled systems: data ingestion (batch/stream via Kafka or ETL), feature engineering pipelines, training infrastructure (distributed compute), experiment tracking systems, model registry, deployment layer (Kubernetes-based serving), and observability stack. The design must ensure scalability, fault tolerance, reproducibility, and low-latency inference. Separation of concerns between offline training and online serving is critical to avoid skew.