As AI and machine learning move from experimentation to production, Kubernetes has become the preferred platform for deploying, scaling, and managing modern AI workloads. However, building a production-ready AI platform requires more than simply running containers—it demands a robust architecture that delivers scalability, resilience, security, and operational excellence.

In this session, you'll learn how to design and operate scalable AI/ML platforms on Kubernetes using cloud-native principles and MLOps best practices. The session covers containerizing machine learning models, deploying inference services, autoscaling workloads, managing GPU resources, implementing observability, and securing AI applications in production. It also explores architectural patterns for integrating CI/CD, model versioning, and governance into enterprise AI platforms.

Through a live demonstration, attendees will see how an AI application can be deployed, scaled, and managed on Kubernetes, highlighting practical techniques for improving performance, reliability, and operational efficiency. Whether you are a Platform Engineer, DevOps Engineer, Machine Learning Engineer, Solution Architect, or Enterprise Architect, this session will provide actionable insights for building production-ready AI platforms that can scale with business demands.