This session delves into scaling a facial recognition system from campus to cloud scale using Kubernetes. It focuses on containerized model training, deployment automation, and infrastructure optimization, utilizing GPU clusters, CI/CD for zero downtime, and MLOps with KServe and Kubeflow. We explore a microservices architecture with declarative APIs linking Feast, Spark, and PostgreSQL for real-time predictions. Attendees will learn Kubernetes architecture and CLI best practices applicable to data transformation, collaboration, model retraining, and versioning. The session also discusses infrastructure sizing, balancing throughput, cost, and accuracy. Practical guides for Kubernetes-based facial recognition, emphasizing portability, fault tolerance, and availability, are provided.