Kubeflow End to End

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Kubeflow End to End

1 시간 30 분 9 크레딧


Google Cloud Self-Paced Labs



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 GKE 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

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