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 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 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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Create a service account
Create a Cloud Storage bucket
Create a cluster
Apply Kubflow with Seldon to the cluster (verify pods)
Apply the component manifests to the cluster in order to launch the training.
Apply the component manifests to the cluster in order to launch the serving