What is a microservices-based ML classification architecture?

A microservices-based ML classification architecture splits model serving, feature engineering, and decision logic into independent services.

In large-scale classification systems, microservices architecture decouples responsibilities into independent deployable units such as feature service, inference service, preprocessing service, and decision orchestration service. This enables independent scaling, faster deployments, and fault isolation. Communication typically happens via REST/gRPC or message queues like Kafka. However, it introduces challenges like network latency, versioning consistency, and distributed debugging complexity. Proper design requires API contracts, schema validation, and centralized observability.