What is the architecture for AI model serving and how do you integrate it with applications?

AI model serving provides scalable, reliable inference endpoints. Architecture: model registry (MLflow, Weights & Biases) stores versioned models with metadata, serving infrastructure (Triton Inference Server, TorchServe, BentoML, or cloud-managed like SageMaker Endpoints) hosts models with auto-scaling, and an API layer exposes standardized endpoints to applications. Deployment patterns: real-time serving (synchronous HTTP/gRPC for interactive applications), batch serving (process large datasets periodically for pre-computation), streaming serving (continuous inference on data streams for real-time analytics). Integration: applications call the serving endpoint via REST/gRPC, sending input data and receiving predictions. Implement client-side retry logic, circuit breakers, and timeout handling. A/B testing: route a percentage of traffic to new model versions, compare business metrics, and promote the winner. Canary deployment: gradually increase traffic to new models while monitoring error rates. Infrastructure: use Kubernetes with custom autoscalers that scale based on inference latency and queue depth, not just CPU utilization. GPU optimization: batch incoming requests to maximize GPU utilization, implement dynamic batching with timeout-based flushing, and use model ensembles (multiple models on one GPU) for small models. Monitoring: track P50/P95/P99 latency, throughput, GPU utilization, model prediction distribution (detect drift), and business KPIs tied to model performance.