This session explores architectural principles for building real-time AI systems on Kubernetes that process streaming data and deliver predictions at scale. We'll examine how to unify streaming infrastructure with ML pipelines while addressing challenges of latency, reliability, and resource efficiency.
Key topics include:

Kubernetes CRDs for declarative streaming management
ML pipeline orchestration patterns with real-time data flows
Resilience strategies: circuit breakers, fallback models, graceful degradation
GPU scheduling and cost optimization models
Multi-tenancy and isolation designs

We'll analyze architectural trade-offs using distributed systems principles like backpressure and flow control. The session provides decision frameworks for technology selection and capacity planning, focusing on timeless patterns rather than specific tools.