What is model serving architecture for classification at scale?

Model serving architecture defines how trained classification models are exposed for real-time or batch predictions at scale.

Production classification systems use layered architecture: API gateway → request validation → feature retrieval → model inference → post-processing → response. Serving can be real-time (low latency REST/gRPC) or batch (Spark jobs). Advanced systems use autoscaling, model caching, GPU inference, and A/B testing frameworks. They also include observability layers for latency, throughput, and prediction distribution monitoring.