AI infrastructure
Why Agentic SREs Require Active Telemetry in Kubernetes
Discover how Active Telemetry enables Agentic SREs to move from reactive firefighting to autonomous diagnosis and proactive reliability in Kubernetes ...
Tucker Callaway | | Active Telemetry, Active Telemetry pipeline, Agentic SRE, AI infrastructure, AI observability, AI-driven SRE, autonomous diagnosis, autonomous operations, cloud native operations, context engineering, data context, intelligent observability, KubeCon 2025, Kubernetes reliability, MTTR reduction, operational autonomy, proactive remediation, root cause analysis, site reliability engineering, telemetry architecture
Why Traditional Kubernetes Security Falls Short for AI Workloads
AI workloads on Kubernetes bring new security risks. Learn five principles—zero trust, observability, and policy-as-code—to protect distributed AI pipelines ...
Ratan Tipirneni | | AI infrastructure, AI security, AI Workloads, cloud native AI, cloud native security, container security, data protection, DevSecOps, edge AI, GPU workloads, KubeCon 2025, kubernetes, Kubernetes observability, Kubernetes security, microsegmentation, multi-cluster security, policy as code, runtime protection, Spectro Cloud report, zero-trust
Fitting Square Kubernetes Into the Round AI-Native Apps
Kubernetes tamed cloud-native workloads, but AI-native apps push its limits. Can it evolve for GPU-first, data-intensive AI — or is it time for new control planes? ...
Alan Shimel | | AI control plane, AI infrastructure, AI pipelines Kubernetes, AI-native applications, cloud-native vs AI-native, container orchestration AI, distributed training orchestration, GPU scheduling, inference at scale, internal developer platforms, Kubeflow, KubeRay, kubernetes, Kubernetes AI workloads, Kubernetes future, Kubernetes limitations, Kubernetes vs AI, platform engineering, Ray on Kubernetes, Volcano scheduler

