Artificial Intelligence (AI) is quickly changing modern enterprises, but harnessing its full potential demands not only excellent models, but infrastructure expertise. Google Kubernetes Engine (GKE) has emerged as a foundation for AI innovation, providing a platform that combines cloud-native flexibility, enterprise-grade security, and seamless access to advanced accelerators. In a recent webinar, I joined Tom Viilo (Head of Alliances) and Guilhem Tesseyre (CTO and Co-Founder) of Zencore for a deep dive into how technical leaders can design, optimize, and operate GKE environments for AI at scale.
Founded by former Google Cloud engineers, Zencore specializes exclusively in Google Cloud, guiding organizations from migration through modernization with insights gained from hands-on delivery. Their deep, practical expertise helps clients overcome common adoption challenges and achieve tangible results at scale.
GKE is a managed Kubernetes offering with AI-friendly capabilities embedded into its architecture, including:
Based on their experience building and deploying AI workloads on GKE, Tom and Guilhem shared some guidance on several key areas to focus on:
Accelerator scarcity can be a significant challenge for organizations relying on specialized hardware, including GPUs. To mitigate this, consider deploying regional clusters that span multiple availability zones, which helps distribute demand and increase the chances of finding available resources. Pre-reserve node pools with commonly used GPU Stock Keeping Units (SKUs) to ensure you have dedicated access when needed. You can also implement fallback mechanisms that allow workloads to gracefully degrade to lower-tier accelerators if the preferred, high-end options are unavailable, ensuring continued operation even under resource constraints.
Addressing resource fragmentation is important for maximizing the efficiency of your Kubernetes clusters. One effective strategy is to deploy node packing algorithms, which intelligently co-locate compatible workloads on the same nodes, ensuring accelerators are used to their fullest potential rather than sitting idle. Additionally, implement custom scheduling solutions, whether through the native kube-scheduler or tools like Karpenter, to intelligently fill gaps in cluster capacity, preventing underutilization and optimizing resource allocation.
To bridge Kubernetes expertise gaps within your team, focus on continuous skill enrichment and proactive risk reduction. Regularly conduct game days that simulate incident response and failover scenarios; these exercises build practical experience and improve your team's ability to react under pressure. Complement this with the adoption of policy-as-code and automation, which standardize configurations and operational procedures, significantly minimizing the risks associated with manual errors and ensuring consistent, reliable cluster management. This reduces day-to-day toil and risk.
To support the diverse needs of modern AI workloads, organizations increasingly need to build architectural patterns that balance scalability, automation, and efficient resource use across the ML lifecycle, from distributed training to flexible model serving. The following are a few suggestions on how to do that:
Once you have your AI workloads up and running on Google Kubernetes Engine, assess where your team’s strengths and focus should lie. If managing GKE clusters, including handling scaling, cost optimization, security, and reliability, is pulling engineers away from model development or customer-facing innovation, it may be time to consider outsourcing infrastructure management. Managed Kubernetes-as-a-Service partners can bring best practices, automation, and proactive support to free your team to focus on core business goals while ensuring production AI workloads remain efficient, secure, and resilient.
The right time to outsource is often when the hidden costs of maintaining and troubleshooting GKE internally outweigh the benefits, or when the pace of business demands more than your current team can sustainably deliver. By partnering with GKE experts, you can maintain full control over your data and ML pipelines, but shortcut complex operational hurdles, thus transforming infrastructure from a distraction into a business accelerator.
Watch the webinar for more details about AI on GKE.
