Kubeflow is a novel open-source tool for end-to-end Machine Learning on top Kubernetes. It has great powers, however, as it is composed of 30+ microservices, it can be challenging to deploy and operate.
In this tutorial, we show how to get started with Kubeflow on Azure Kubernetes Service (AKS) in a few simple steps.
What you’ll learn
- How to deploy Kubeflow on top of AKS
- How to observe the state of your deployment using Juju
- How to access your Kubeflow dashboard from your local machine
What you’ll need
- Access to an AKS Kubernetes cluster via
- A minimum of 4 CPU, 16GB RAM, 50GB Disk available in your cluster
- Some basic command-line knowledge