Explore the fundamentals of KubeFlow and its applications in machine learning with Kubernetes in this informative conference talk. Gain insights into container technology, Kubernetes' desired state management, and the core components of KubeFlow. Learn about collaborative filtering, rating matrices, and the use of Minio for centralized storage. Discover how to implement machine learning models using Jupiter notebooks, convert them to MLJobs, and deploy them for machine serving. Follow a practical demonstration of a recommender system for product suggestions based on customer purchase history. Delve into advanced topics such as concept drift, continuous model updates, and custom components like Argo workflow. Get hands-on experience with code samples and learn how to engage with the KubeFlow community. Master the essentials of KubeFlow, machine learning, and their integration with Kubernetes for efficient deployment of complex workloads across private and public clouds.