Building an AI application locally is easy. Running it reliably in production is where cloud-native engineering begins.

In this hands-on session, we’ll take a simple Gemini-powered application from a local prompt to a containerized, production-ready workload running on Kubernetes. We’ll explore how Docker and Kubernetes provide the infrastructure needed to package, configure, deploy, scale, and operate modern AI applications without requiring a complex machine-learning platform.

Through a practical demo, we’ll containerize the application, create Kubernetes Deployments and Services, securely manage API keys using Secrets, configure the application with ConfigMaps, add health checks, and explore horizontal scaling. We’ll also discuss how the same open-source architecture can move from a local Kubernetes environment to Google Kubernetes Engine (GKE).

**What You’ll Learn:**

* Build a simple Gemini-powered application
* Containerize AI workloads with Docker
* Deploy and expose applications on Kubernetes
* Manage configuration and secrets securely
* Add health checks and scaling
* Understand the path from local Kubernetes to GKE