Tigera: From Experimentation to Production: What It Really Takes to Operationalize AI on Kubernetes

Every organization can get a model running in a notebook. The hard part is everything that comes after: promoting that model to a production service that is secure, observable, resilient, and governed. The gap between “it works on my laptop” and “it runs in production” is where most AI initiatives stall—not because of model quality, but because the platform is not ready. Security policies are missing, there is no visibility into model-to-service traffic, identity and access controls are an afterthought, and when something breaks at 2 AM, nobody knows where to look.

This webinar outlines the AI maturity journey from experimentation to production on Kubernetes. We demonstrate how security, observability, and policy enforcement create the operational foundation required for production AI.

• The AI Maturity Gap: Identify the most common platform gaps that stall AI initiatives between experimentation and production—and understand why model quality alone does not determine success.
• Security as an Enabler, Not a Gate: Learn how zero-trust networking and automated policy enforcement let organizations move AI workloads to production faster by building security into the platform rather than bolting it on at the end.
• Observability Across the AI Lifecycle: See how end-to-end visibility—from training data flows to inference endpoint traffic—gives operations teams the confidence to support AI workloads in production without flying blind.
• Building a Platform That Scales With Your AI Ambitions: Explore the architectural patterns that allow organizations to grow from a handful of models to hundreds—with consistent governance, multi-tenancy, and operational controls that keep pace.

Join us to learn what separates organizations that operationalize AI successfully from those that stay stuck in experimentation—and how to build the Kubernetes platform that gets you there.