Running AI workloads on Google Kubernetes Engine (GKE) presents unique challenges, especially for securing the right hardware. Whether you’re dealing with unpredictable demand and varying job durations or simply looking to control costs, this session will equip you with the knowledge and tools to make informed decisions about your GKE AI infrastructure. We’ll explore recent advancements in Dynamic Workload Scheduler, custom compute classes, and Kueue, demonstrating how these technologies can help you effectively access and manage diverse hardware resources.

According to Andreessen Horowitz, 93% of Fortune 500 companies surveyed currently use three or more AI model providers, and now AI agents will proliferate and become standard. With the massive shift in enterprise AI adoption and the pace of change, enterprises need to modernize their network and security to unlock the value that AI generates and keep pace with rapid changes. Learn how to make your network and security AI-ready, plus discover new innovations to reduce infrastructure costs, improve user experience, enable faster developer velocity, and protect your AI infrastructure from new security threats.