In this session, we will follow through an example on how to deploy, train, and serve up a trained model all running in Kubernetes. We will go into details on what the workflow is doing under the hood to understand more of the magic that is happening.
At the end of the demo, you will learn how to deploy a working Kubeflow setup, train, and serve up requests via a webpage
I am a DevOps software engineer that helps small to medium-sized start-ups run large-scale, reliable applications. I work with the entire development team architecting, designing, building, optimizing, and operating infrastructure in the cloud (AWS, Google Cloud, and Azure).
My specialties are Docker, Kubernetes, systems automation, security, and migrating workloads to container-based deployments. In addition to helping customers build and deploy applications, I write for various blogs to help the community use the Kubernetes base infrastructure.