Kubeflow End to End
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 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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