What is Online Inference?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Build a three-layer serving architecture: load balancing layer (NGINX, Envoy, or cloud ALB distributing requests across model replicas with health-check-based routing), model serving layer (TensorFlow Serving, NVIDIA Triton, or Ray Serve hosting optimized models with automatic batching and GPU management), and feature retrieval layer (Redis or DynamoDB serving precomputed features with sub-5ms latency). Optimize the critical path: precompute features in batch pipelines rather than computing at request time, use model compilation tools (TensorRT, ONNX Runtime) for 2-4x speedup, and implement connection pooling to eliminate handshake overhead. For p99 latency under 100ms, co-locate model serving and feature store in the same availability zone. Scale horizontally using Kubernetes HPA based on request queue depth rather than CPU utilization for more responsive auto-scaling.

Implement four reliability patterns: auto-scaling with predictive policies (scale up based on historical traffic patterns 10 minutes before expected peaks, supplemented by reactive scaling on queue depth), circuit breakers (stop sending requests to degraded replicas and route to healthy ones using Istio or Envoy), graceful degradation (serve simpler fallback models or cached predictions when primary model capacity is exhausted rather than returning errors), and request prioritization (queue high-value requests ahead of bulk or lower-priority traffic during capacity constraints). Load test regularly simulating 2x peak traffic to validate scaling behavior. Set SLOs for p50 (under 30ms), p95 (under 80ms), and p99 (under 200ms) latency targets, with automated alerting when any percentile breaches for 5+ consecutive minutes.

Build a three-layer serving architecture: load balancing layer (NGINX, Envoy, or cloud ALB distributing requests across model replicas with health-check-based routing), model serving layer (TensorFlow Serving, NVIDIA Triton, or Ray Serve hosting optimized models with automatic batching and GPU management), and feature retrieval layer (Redis or DynamoDB serving precomputed features with sub-5ms latency). Optimize the critical path: precompute features in batch pipelines rather than computing at request time, use model compilation tools (TensorRT, ONNX Runtime) for 2-4x speedup, and implement connection pooling to eliminate handshake overhead. For p99 latency under 100ms, co-locate model serving and feature store in the same availability zone. Scale horizontally using Kubernetes HPA based on request queue depth rather than CPU utilization for more responsive auto-scaling.

Implement four reliability patterns: auto-scaling with predictive policies (scale up based on historical traffic patterns 10 minutes before expected peaks, supplemented by reactive scaling on queue depth), circuit breakers (stop sending requests to degraded replicas and route to healthy ones using Istio or Envoy), graceful degradation (serve simpler fallback models or cached predictions when primary model capacity is exhausted rather than returning errors), and request prioritization (queue high-value requests ahead of bulk or lower-priority traffic during capacity constraints). Load test regularly simulating 2x peak traffic to validate scaling behavior. Set SLOs for p50 (under 30ms), p95 (under 80ms), and p99 (under 200ms) latency targets, with automated alerting when any percentile breaches for 5+ consecutive minutes.

Build a three-layer serving architecture: load balancing layer (NGINX, Envoy, or cloud ALB distributing requests across model replicas with health-check-based routing), model serving layer (TensorFlow Serving, NVIDIA Triton, or Ray Serve hosting optimized models with automatic batching and GPU management), and feature retrieval layer (Redis or DynamoDB serving precomputed features with sub-5ms latency). Optimize the critical path: precompute features in batch pipelines rather than computing at request time, use model compilation tools (TensorRT, ONNX Runtime) for 2-4x speedup, and implement connection pooling to eliminate handshake overhead. For p99 latency under 100ms, co-locate model serving and feature store in the same availability zone. Scale horizontally using Kubernetes HPA based on request queue depth rather than CPU utilization for more responsive auto-scaling.

Implement four reliability patterns: auto-scaling with predictive policies (scale up based on historical traffic patterns 10 minutes before expected peaks, supplemented by reactive scaling on queue depth), circuit breakers (stop sending requests to degraded replicas and route to healthy ones using Istio or Envoy), graceful degradation (serve simpler fallback models or cached predictions when primary model capacity is exhausted rather than returning errors), and request prioritization (queue high-value requests ahead of bulk or lower-priority traffic during capacity constraints). Load test regularly simulating 2x peak traffic to validate scaling behavior. Set SLOs for p50 (under 30ms), p95 (under 80ms), and p99 (under 200ms) latency targets, with automated alerting when any percentile breaches for 5+ consecutive minutes.