Scalable Machine Learning Model Serving with Azure Kubernetes Service

Question

Pulumi · Accepted Answer

To accomplish scalable machine learning model serving with Azure Kubernetes Service (AKS), we will create a Kubernetes cluster where you can deploy your machine learning models as services. AKS simplifies deploying a managed Kubernetes cluster in Azure by offloading the operational overhead to Azure. Once set up, you can scale your service as needed, and AKS will ensure that it manages the infrastructure for you.

Here's a high-level overview of the approach we're going to take:
1. Create an AKS cluster.
2. Define node pools for our AKS cluster.
3. Configure necessary AKS settings, such as network profiles, service principals, and access profiles.
4. Deploy our machine learning workload onto AKS.

Let's start by writing a Pulumi program in Python that details each of these steps.

First, we'll initialize a new project and create a `__main__.py` file which will contain our code. Ensure that you have the `pulumi_azure_native` package installed using `pip` before implementing the code below.

```python
import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native.resources import ResourceGroup
from pulumi_azure_native.containerservice import ManagedCluster, ManagedClusterServicePrincipalProfile

# Create an Azure Resource Group
resource_group = ResourceGroup('rg')

# Define the ManagedCluster resource for AKS
managed_cluster = ManagedCluster(
    'aks',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    dns_prefix="akskubedns",
    agent_pool_profiles=[{
        'count': 3,  # Start with 3 nodes
        'max_pods': 110,  # Set the maximum number of pods per node
        'mode': 'System',  # The mode of the node pool, System or User
        'name': 'agentpool',  # The name of the node pool
        'vm_size': 'Standard_DS2_v2',  # The size of the VMs in the node pool
    }],
    service_principal_profile=ManagedClusterServicePrincipalProfileArgs(
        client_id="your-service-principal-client-id",
        secret="your-service-principal-secret"
    ),
    # Additional configurations can be provided here
)

# Export the AKS cluster name and the Kubernetes configuration
pulumi.export('cluster_name', managed_cluster.name)
pulumi.export('kubeconfig', managed_cluster.kube_config_raw)
```

This program sets up an AKS cluster that you can use to host and serve your machine learning models. A few notes on what each piece is doing:

- **Resource Group**: Resource groups in Azure act as a logical container into which Azure resources like web apps, databases, and storage accounts are deployed and managed. 
- **ManagedCluster**: This resource defines the AKS cluster. In the `agent_pool_profiles`, we specify the desired count of VMs, the size of the VM instances to deploy, and other settings that determine how the cluster will operate. For machine learning workloads, you may want to choose a VM size that has GPUs if you need to do intensive computation.
- **Service Principal**: AKS uses a service principal to create and manage resources in Azure. This service principal grants AKS permission to Azure resources. You'll need to create one and provide its client ID and secret to the cluster configuration.
  
Please replace the `your-service-principal-client-id` and `your-service-principal-secret` with the credentials of the service principal you have designated for AKS.

At the end of the script, we export the cluster name and raw Kubernetes configuration which you can use with `kubectl` to deploy and manage applications on the cluster, including serving machine learning models.

Scaling up the node count in the `agent_pool_profiles` or introducing additional node pools are actions you might take once your machine learning model demands it, via updates to the Pulumi program.

Once your infrastructure is deployed, you would continue to deploy your machine learning models onto AKS. You could use Kubernetes deployments and services to serve your model behind a REST API, for instance. The model serving component would typically be a Docker container that encapsulates your model and the server running it (for example, using Flask or FastAPI for Python models).

Keep in mind that managing the deployments and ingesting the `kubeconfig` for Kubernetes management lies outside the scope of the Pulumi script and pertains to the Kubernetes ecosystem. You would typically use `kubectl`, the Kubernetes CLI, or another continuous delivery tool for this.