GSP221

Introduction

Kubeflow is a machine learning toolkit for Kubernetes. The project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable. The goal is to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.

A Kubeflow deployment is:

• Portable - Works on any Kubernetes cluster, whether it lives on Google Cloud Platform (GCP), on-premise, or across providers.
• Scalable - Can utilize fluctuating resources and is only constrained by the number of resources allocated to the Kubernetes cluster.
• Composable - Enhanced with service workers to work offline or on low-quality networks

Kubeflow will let you organize loosely-coupled microservices as a single unit and deploy them to a variety of locations, whether that's a laptop or the cloud. This codelab will walk you through creating your own Kubeflow deployment.

What you'll build

In this lab you're going to build a web app that summarizes GitHub issues using a trained model. Upon completion, your infrastructure will contain:

• A Kubernetes Engine cluster with standard Kubeflow and Seldon Core installations.
• A training job that uses Tensorflow to generate a Keras model.
• A serving container that provides predictions.
• A UI that uses the trained model to provide summarizations for GitHub issues.

What you'll learn

• How to install Kubeflow.
• How to run training using the Tensorflow job server to generate a Keras model.
• How to serve a trained model with Seldon Core.
• How to generate and use predictions from a trained model.

What you'll need

• A basic understanding of Kubernetes.
• A GitHub account.