Tutorial: Kubeflow End-to-End: GitHub Issue Summarization
IMPORTANT NOTE:Due to the nature of tutorials, this session has been placed in a smaller capacity room to help facilitate a conducive learning environment. Space is very limited and seating will be g …
|Tutorial: Kubeflow End-to-End: GitHub Issue Summarization
|Steve Greenberg (Tutorial Assistant, Google), Gonzalo Gasca Meza (Tutorial Assistant, Google), Michelle Casbon (Senior Engineer, Google), Amy Unruh (Developer Relations Engineer, Google)
|KubeCon + CloudNativeCon North America
|Seattle, WA, USA
|Dec 9-14, 2018
IMPORTANT NOTE: Due to the nature of tutorials, this session has been placed in a smaller capacity room to help facilitate a conducive learning environment. Space is very limited and seating will be given on a first come-first serve basis. The tutorial will be recorded and viewed on the CNCF YouTube channel after the event concludes. Thank you for your understanding.Kubeflow is an OSS machine learning stack that runs on Kubernetes.In this session, you will learn how to install and use Kubeflow to support a full ML workflow.You’ll build an automatic summary generator using a public dataset of GitHub Issues. In the process, you’ll install Kubeflow from scratch, preprocess your dataset, then perform training of a TensorFlow NLP model. You’ll then evaluate your trained model, serve it, and interact with the prediction endpoint from a web front-end.You will become familiar with Google Cloud Platform and OSS tools and services such as Apache Beam, TFX, Cloud Shell, Kubernetes Engine, Cloud Storage, and Container Registry. All components are built from source in the Kubeflow Examples repository and are directly transferable to other environments (local, on-prem, and other cloud providers).Prerequisite: familiarity with Kubernetes.