Real Time Model Serving at Scale Questions

Designing low latency, high throughput inference services and the operational patterns needed to serve models at product scale. Topics include batching and caching strategies to balance latency and throughput, autoscaling and resource allocation, model versioning and canary rollouts, techniques to serve multiple models or model ensembles, compression and quantization trade offs, instrumentation and observability for inference pipelines, and operational considerations such as cost, latency budgets, and fault tolerance.