Google Kubernetes Engine – The Container Platform for AI at Scale from Google Cloud

Brandon Royal, a Product Manager at Google Cloud, describes how Kubernetes can be leveraged for AI applications, particularly focusing on model training and serving. He begins by emphasizing the growing importance of generative AI across many organizations, highlighting that Google Kubernetes Engine (GKE) provides a robust platform for integrating AI into products and services. The platform is designed to handle the increasing complexity and scale of AI models, which demand high efficiency and cost-effectiveness. Royal mentions that GKE, often referred to as the operating system of Google’s AI hypercomputer, orchestrates workloads across storage, compute, and networking to deliver optimal price performance.

Royal addresses the challenges of scaling AI workloads, noting that model sizes are growing and pushing the limits of infrastructure. To tackle these challenges, GKE offers several optimizations, such as dynamic workload scheduling and container preloading, which enhance the efficiency and utilization of AI resources like CPUs, GPUs, and TPUs. He introduces the concept of “good put,” a metric for measuring machine learning productivity, which includes scheduling good put, runtime good put, and program good put. These metrics help ensure that resources are utilized effectively, minimizing idle time and maximizing forward progress in model training. Royal also highlights the importance of leveraging open-source frameworks like Ray and Kubeflow, which integrate seamlessly with GKE to provide a comprehensive AI development and deployment environment.

The presentation includes a demo showcasing the optimization capabilities of GKE. Royal demonstrates how container preloading and persistent volume claims can significantly reduce the time required to deploy AI models. By preloading container images and sharing model weights across instances, GKE can cut down deployment times from several minutes to mere seconds. This optimization is crucial for large-scale AI deployments, where efficiency and speed are paramount. Royal concludes by encouraging the audience to explore the resources and tutorials available for building AI platforms on GKE, emphasizing that these optimizations can provide a competitive edge in the fast-evolving field of AI.