Effortless AI Inference: Deploying and Scaling With GKE Reference Architecture

The challenge for every IT leader is simple: Your AI projects are ready to scale, but your infrastructure isn't. High-performance inference demands agility, fault tolerance, and cost efficiency—all while keeping data governance tight. Piecing together a homegrown solution is slow, resource-intensive, and introduces unnecessary risk.

This webinar will walk you through the Google Kubernetes Engine (GKE) Reference Architecture —a standardized, proven framework for deploying and managing inference at massive scale. You'll get the tactical knowledge needed to empower your data science teams while delivering the operational stability your business demands.

• Maximize GPU value: learn resource allocation strategies that reduce idle time and cut operational costs
• Ensure enterprise reliability: deploy a proven architecture that guarantees high availability and automates operational tasks to keep mission-critical services running
• Accelerate time-to-market: standardize your deployment pipeline to move models into production significantly faster—cutting weeks off typical delivery cycles
• Simplify governance & compliance: leverage GKE's built-in controls for unified security and compliance across all workloads
• Protect and empower teams: give your data scientists the freedom to iterate quickly while maintaining the stable, unified production environment

Join us and transform model deployment from a bottleneck into a competitive advantage.