# Overview

In this self-paced exercise, you will first provision a GPU enabled AKS cluster with a custom cluster blueprint that contains the Nvidia GPU Operator as an addon. You will then follow that up by deploying a workload that will use the GPU and review the integrated dashboards for GPUs.

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## What Will You Do by Part

| Part | What will you do? |
| --- | --- |
| 1 | [Setup](https://docs.rafay.co/learn/quickstart/aks/gpu/setup/) and Configuration |
| 2 | [Provision](https://docs.rafay.co/learn/quickstart/aks/gpu/provision/) an Azure AKS Cluster with GPUs |
| 3 | Create a Cluster [Blueprint](https://docs.rafay.co/learn/quickstart/aks/gpu/blueprint/) with the Nvidia GPU Operator |
| 4 | Deploy a Machine Learning [Workload](https://docs.rafay.co/learn/quickstart/aks/gpu/workload/) to the GPU enabled AKS Cluster |
| 5 | [Deprovision](https://docs.rafay.co/learn/quickstart/aks/gpu/deprovision/) the AKS cluster |

Watch a video showcasing the end-to-end experience below.

AI/ML Workloads on Azure AKS with NVIDIA GPUs \| Rafay Kubernetes Operations - YouTube

Tap to unmute

[AI/ML Workloads on Azure AKS with NVIDIA GPUs \| Rafay Kubernetes Operations](https://www.youtube.com/watch?v=9o3yRiqKHDQ)

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## Assumptions

- You have a Git client on your laptop.

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