AI Infrastructure Best Practices - Enterprise Do's and Don'ts

Explore enterprise AI infrastructure best practices in this 41-minute panel discussion featuring industry experts who address critical decisions facing organizations deploying AI workloads on Kubernetes. Learn from power users with extensive AI deployment experience, builders of modern Kubernetes-based AI frameworks like Ray, and practitioners managing heterogeneous AI use cases for enterprise environments. Discover whether AI workloads introduce unique enterprise readiness requirements beyond traditional Kubernetes considerations like logging, monitoring, analytics, security, and multi-tenancy. Examine strategies for managing high costs and limited availability of hardware accelerators such as GPUs, and evaluate architectural decisions including whether to implement siloed stacks for pre-training, post-training, serving, and batch workloads versus consolidating multiple stacks on single clusters. Consider cluster sizing approaches comparing large numbers of small clusters against small numbers of large clusters, and assess deployment strategies for single versus multi-region, multi-cloud, and neo-cloud federation scenarios.