How to Run Intelligent AI Apps on Kubernetes with AKS

Yes, you can run smart AI apps on Kubernetes with AKS. Smart apps use AI or machine learning to give cool features like suggestions or talking back in normal language. AKS gives you a managed platform that makes growing and connecting AI easy.

• AKS works well with Azure services for AI jobs.

• The KAITO add-on lets you run AI jobs with GPUs and handle GPU drivers easily.

• AKS supports both sideways and upwards scaling for your AI-powered Kubernetes projects.

Key Takeaways

• AKS makes it easier to set up AI apps. It takes care of Kubernetes for you. You can spend more time making smart features.

• AKS lets you use GPU support. This helps your AI models work faster. It also helps your apps grow when needed.

• You can use autoscaling tools like Horizontal Pod Autoscaler. These tools help your AI apps handle different amounts of work. They make sure your apps work well all the time.

• Keep your apps safe by using Azure RBAC. Store secrets in Azure Key Vault. This keeps your data and resources safe.

• Use tools like Azure Monitor to check how your app is doing. These tools help your AI apps run well and stay safe.

AI-Powered Kubernetes Overview

Why Use AKS for AI

AI-Powered Kubernetes helps make apps smarter and work better. AKS is a managed service that runs Kubernetes for you. You do not need to set up everything yourself. This platform gives you tools to build, train, and use AI models for many users.

Here are some main benefits of using AI-Powered Kubernetes for your smart apps:

You can also use dynamic resource allocation. This means your app gets more power when it needs it. It saves resources when it does not need them. AKS keeps each workload apart, so one app will not slow down another.

Key Features

AKS has many features that help with AI and machine learning:

AKS supports GPU acceleration, so AI models run faster. You can make node pools with strong GPUs and use autoscaling for more users or bigger jobs. Azure AI services let you use ready-made models or train your own. DevOps tools help you manage your AI apps with less effort.

Tip: With AKS, you can spend time making smart features. The platform takes care of the hard work.

Setup and Prerequisites

Azure Resources

You need some Azure resources before you build AI apps on AKS. These resources help you make and manage your Kubernetes cluster for AI jobs. Here are the steps to begin:

1. Create a Resource Group
   Use the Azure CLI to make a resource group. This group keeps all your resources together.

az group create --name myResourceGroup --location eastus

1. Deploy an AKS Cluster
   Set up your AKS cluster with the right options. Turn on OIDC issuer and workload identity for better safety.

az aks create \
  --resource-group myResourceGroup \
  --name myAKSCluster \
  --enable-oidc-issuer \
  --enable-workload-identity

1. Create a Managed Identity
   Make a managed identity for your cluster. This helps your apps connect to other Azure services safely.

az identity create --name myAksIdentity --resource-group myResourceGroup

Tip: Always use managed identities for safe access to Azure resources.

Cluster Configuration

After you set up your Azure resources, you need to get your AKS cluster ready for AI jobs. These steps help you prepare your cluster for big jobs and GPU support.

1. Check Prerequisites
   Make sure you have a machine learning workspace and a Kubernetes cluster.

2. Set Up Variables
   Pick the names and locations you need for your setup.

3. Create a Node Pool
   Add a special node pool for your AI jobs. You can use GPU virtual machines for faster work.

az aks nodepool add \
  --resource-group myResourceGroup \
  --cluster-name myAKSCluster \
  --name gpu-pool \
  --node-count 1 \
  --node-vm-size Standard_NC6

1. Install the Azure ML Extension
   Add the Azure Machine Learning extension to your AKS cluster. This helps you run and manage machine learning jobs.

2. Attach the Kubernetes Compute Target
   Connect your AKS cluster to your machine learning workspace. This lets you use the cluster for training and deploying models.

To use GPU-based AI jobs, you need to do a few more things:

• Create a GPU-Enabled Instance Type
  Make a custom instance type with a YAML file. Use kubectl to apply it so your cluster can use GPUs.

• Install the NVIDIA Device Plugin
  Run this command to turn on GPU access in your AKS cluster:

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.13.0/nvidia-device-plugin.yml

• Deploy the Azure Machine Learning Extension
  Use the Azure CLI to add the extension. This makes sure your cluster is ready for training.

You can check your cluster nodes with:

kubectl get nodes

Scaling Options for AI Workloads

AKS gives you different ways to scale your AI apps. Here is a table that shows the main choices:

Note: With these scaling tools, your AI-Powered Kubernetes apps can run well, even when demand goes up or down.

Deploying AI Models

Using KAITO

You can use KAITO to put big language models and AI agents on AKS. KAITO makes this easy and quick. You follow steps to get your model working:

1. Turn on KAITO as a cluster extension with Azure CLI.

2. Set up your kubectl tool to link to your cluster.

3. Check your link by listing the nodes.

4. Use a YAML file to deploy a hosted AI model.

5. Watch for changes in your workspace resources.

6. Find the IP address for the inference service.

7. Try out the inference service with a sample input.

8. Remove resources when you are done.

Here are some commands you use in these steps:

az aks update --name $CLUSTER_NAME --resource-group $AZURE_RESOURCE_GROUP --enable-ai-toolchain-operator --enable-oidc-issuer

kubectl apply -f https://raw.githubusercontent.com/kaito-project/kaito/refs/heads/main/examples/inference/kaito_workspace_phi_4_mini.yaml

kubectl get svc workspace-phi-4-mini -o jsonpath='{.spec.clusterIP}'

KAITO gives you features that help you handle AI models on AKS:

KAITO works well with AI-Powered Kubernetes. You get an easy time when you deploy and grow your smart apps.

RAG and vLLM Integration

You can make your AI apps smarter by using RAG and vLLM. RAG helps your models find and use facts from outside sources. vLLM makes your models answer faster.

RAG lets your app look for facts or documents before it answers. You link your model to tools like Azure AI Search or other APIs. This way, your app finds good information and uses it to reply.

vLLM helps your models answer quickly. You use vLLM with AKS to handle lots of requests at once. Your users get fast replies, even when many people use your app.

The Model Context Protocol (MCP) changes how AI apps connect to outside tools and data. You can put MCP clients and servers on Kubernetes to handle big AI systems.

APIs help you get context from outside sources. Azure OpenAI and Azure AI Search have APIs that fetch data and work with MCP servers.

You use RAG and vLLM together to build apps that answer questions with new information. Your users get smart and quick replies.

Custom Models

You can put your own machine learning models on AKS. You follow clear steps to make sure your models work well:

1. Make sure DNS works for the AKS API server.

2. Make sure DNS works for Microsoft Container Registry to get Docker images.

3. Download images from MCR, making sure you can connect out.

4. Make a deployment setup that lists compute resources like cores and memory.

5. Make an inference setup for your model and web service.

6. Make sure your AKS node can use DNS for your Azure Container Registry and Azure Blob storage to get models.

7. Set up autoscaling if you need it, including target use and replica counts.

You can link your custom models to outside AI APIs. This helps your app use more data and tools. You use APIs to get context and make your model’s answers better.

The Model Context Protocol (MCP) helps you link your models to outside data and tools. You use APIs from Azure OpenAI and Azure AI Search to get information and connect with MCP servers.

You build flexible AI apps by mixing your models with outside APIs. Your app learns from new data and gives better answers.

MLOps and Security

Model Lifecycle

When you manage AI models on AKS, you need to use smart steps. You want your models to work well and stay safe. Here are some good things to do:

• Keep track of changes to your models. Use model management and versioning. This helps you know which version works best.

• Use automation to make your work easier. It helps you avoid mistakes and saves time. Automation keeps your process smooth.

• Make sure your system can grow when more people use it. Good resource management helps your app run fast.

• Protect your data and follow security rules.

You can use the Azure ML Model Registry to store your trained models. When you register a model, you keep all files together. This makes it easy to deploy or update your models. You should watch your models for problems like model drift or slow speed. Use Azure ML Pipelines to automate training and deployment. Limit who can use your models with Azure RBAC. Only the right people should have access.

Tip: Automation and monitoring help your AI apps run well and stay safe.

Securing Workloads

You must keep your AI workloads safe on AKS. Security protects your data and keeps your app working. Follow these steps to make your security better:

1. Set up access control so only trusted users can reach your resources.

2. Use network policies and Azure Firewall to control traffic in and out of your cluster.

3. Scan for weak spots and fix any problems in your images and nodes.

4. Store secrets in Azure Key Vault and handle Kubernetes secrets carefully.

5. Turn on logging and monitoring to find problems early. Azure Security Center helps you spot issues fast.

6. Check your security rules often and change them if needed.

7. Keep important services separate to lower risks for sensitive workloads.

Microsoft Defender works with AKS to make your security stronger. Here is how it helps:

Note: Strong security and good model management help you build trustworthy AI apps on AKS.

Real-World Use Cases

Enterprise Applications

AKS helps companies run smart apps. These apps solve problems and make work easier. Here are some examples of AI apps that use AKS:

You can build engines that suggest products to people. Computer vision APIs help scan pictures or videos for details. These apps use AKS to handle lots of data and give quick answers.

Many companies pick AKS because it runs AI models for many users, keeps data safe, and lets you update apps fast.

Scaling and Monitoring

When you run AI jobs on AKS, you want your apps to stay quick and work well. You can use different ways to scale and watch your AI systems:

• Use autoscaling to add more power when your app gets busy.

• Give each job the right CPU and memory.

• Use availability zones so your app keeps working if one zone fails.

• Change your setup for different jobs, like training or serving models.

• Use tools like Sedai to automate tasks and save time.

For computer vision APIs, use Azure Monitor to check how your app works. Azure Cost Management helps you track spending. Security tools keep your data safe. Docker containers make your app easy to move and update. Azure DevOps lets you automate updates so your app always has new features.

Good scaling and monitoring help your AI apps work well and stay safe. You can build smart features while AKS does the hard work.

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You can make smart AI apps on AKS if you follow easy steps. First, use tools like Managed Prometheus to watch your app. Next, train models that can spot problems. Then, build AI agents that fix issues when they find them. Use large language models to help make your plan better. Check how well you do by looking at things like saving money, making good choices, and getting more work done.

You should try putting your own AI apps on AKS. Play around with KAITO, RAG, and MLOps to find new ways to use AI.

FAQ

What is AKS and why should I use it for AI apps?

AKS stands for Azure Kubernetes Service. You use it to run and manage containers in the cloud. It helps you deploy, scale, and update AI apps easily. You do not need to manage servers yourself.

How do I add GPU support to my AKS cluster?

You add GPU support by creating a GPU node pool. Use the Azure CLI to set up nodes with GPU power. This lets your AI models run faster. You can also install the NVIDIA device plugin for better performance.

Can I use my own AI models on AKS?

Yes, you can deploy your own AI models. Package your model in a container. Set up the deployment using YAML files. AKS lets you connect your model to other Azure services for more features.

What tools help me monitor my AI workloads on AKS?

You can use Azure Monitor to track your app’s health. Set up alerts for issues. Use dashboards to see how your AI jobs perform. Monitoring helps you fix problems quickly and keep your app running well.

How do I keep my AI workloads secure on AKS?

You control access with Azure RBAC. Store secrets in Azure Key Vault. Use Microsoft Defender for threat detection. Set up network policies to block unwanted traffic. Regularly check your security settings to protect your data.