Distributed Machine Learning Model Serving with Azure Cache for Redis

Question

Pulumi · Accepted Answer

To serve a distributed machine learning (ML) model using Azure Cache for Redis as a backend, your Pulumi program will need to perform several tasks:

1. Provision an Azure Cache for Redis instance
2. Deploy a compute resource that will serve your ML model (e.g., Azure Kubernetes Service or Azure Functions, depending on your architecture and scaling needs)
3. Configure the compute resource to communicate with the Redis instance
4. Deploy the ML model to the compute resource

For the sake of simplicity, we will use an Azure Kubernetes Service (AKS) in this example to serve the model with an API, and the model will use Azure Cache for Redis for low-latency, high-throughput data access.

Before writing the Pulumi program, make sure you have the Pulumi CLI and Azure CLI installed and configured. The following code assumes you have already logged in to Azure and set up your Azure credentials for Pulumi.

Here is a Pulumi program written in Python, which sets up these services:

```python
import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native import resources, cache, containerservice
from pulumi_azure_native.containerservice import ManagedCluster, ManagedClusterAgentPoolProfileMode

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

# Create an Azure Cache for Redis instance
redis_cache = cache.Redis(
    "redisCache",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=cache.SkuArgs(
        name="Basic",  # Choose the SKU that best fits your needs
        family="C0",
        capacity=0,
    ),
    # See the Azure-native documentation for additional configuration options:
    # https://www.pulumi.com/registry/packages/azure-native/api-docs/cache/redis/
)

# Create an Azure Kubernetes Service
aks_cluster = containerservice.ManagedCluster(
    "aksCluster",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    agent_pool_profiles=[ManagedClusterAgentPoolProfileArgs(
        mode=ManagedClusterAgentPoolProfileMode.SYSTEM,
        count=3,
        vm_size="Standard_DS2_v2",  # Choose the VM size that best fits your needs
        os_type="Linux",
        name="agentpool",
    )],
    dns_prefix=f"aks-{resource_group.name}",
    # See the AKS documentation for additional configuration options:
    # https://www.pulumi.com/registry/packages/azure-native/api-docs/containerservice/managedcluster/
)

# Outputs
pulumi.export('resource_group_name', resource_group.name)
pulumi.export('redis_cache_hostname', redis_cache.hostname)
pulumi.export('aks_cluster_name', aks_cluster.name)
```

In this Pulumi program, we do the following:

1. **Resource Group**: Create a new Azure Resource Group, which will contain all of our resources.
2. **Azure Cache for Redis**: Provision an Azure Cache for Redis instance in the Basic SKU (you might want to select a different SKU based on your performance and pricing requirements). You can find more details on configuration options in the [Azure-native Redis documentation](https://www.pulumi.com/registry/packages/azure-native/api-docs/cache/redis/).
3. **Azure Kubernetes Service (AKS)**: Deploy an AKS cluster to serve our ML model via an API. The cluster uses a "Standard_DS2_v2" VM size and has 3 nodes. Modify the SKU and capacity to fit your workload needs. You can find more details on AKS configuration in the [AKS documentation](https://www.pulumi.com/registry/packages/azure-native/api-docs/containerservice/managedcluster/).

Keep in mind that for a real-world scenario, you would also need to:

- Configure networking, firewall rules, and security, which are not covered in this example.
- Deploy your machine learning model as a containerized application to the AKS cluster. This typically involves creating a Docker image with your model and Kubernetes manifests.
- Set up communication between your model-serving application and the Redis instance for model predictions caching or feature store.

After running this Pulumi program, you will have a Redis instance and an AKS cluster ready. The next steps would involve deploying your actual ML model-serving software to the Kubernetes cluster, which is beyond the scope of this example but can be accomplished using Pulumi as well.