1. Hybrid Cloud Infrastructure for AI Model Serving


    To set up a hybrid cloud infrastructure for AI model serving, one typically needs resources that enable the deployment and serving of machine learning models, such as container services to run the model serving application, object storage to hold the model files, and possibly a managed AI platform to facilitate easier deployment and operations.

    Given that we are creating a hybrid cloud infrastructure, we would need resources that support multi-cloud or cross-cloud operations. However, the specifics of the setup would depend on the exact requirements, such as the cloud providers being used, the scale of operation, security considerations, etc.

    Below is a Python program using Pulumi to create a basic AI model serving infrastructure. In this program, we'll create resources in Azure, which supports hybrid cloud infrastructure, leveraging Azure Machine Learning Services and Azure Kubernetes Service, which can integrate with on-premises or other cloud resources.

    Firstly, here's what we will do step-by-step:

    1. We will create an Azure Machine Learning workspace, which is a foundational block for machine learning workflows.
    2. We will then create a Kubernetes cluster using Azure Kubernetes Service (AKS) which can be used to deploy our model serving applications.
    3. Lastly, we will set up an Azure Container Registry to store and manage container images that we'll use for serving our AI models.

    Let's move on with the Pulumi program:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group for organizing resources resource_group = azure_native.resources.ResourceGroup('ai-model-serving') # Create an Azure ML Workspace ml_workspace = azure_native.machinelearningservices.Workspace( 'mlWorkspace', resource_group_name=resource_group.name, location=resource_group.location.apply(lambda loc: loc if loc else 'East US'), # Default to East US if location is not set sku=azure_native.machinelearningservices.SkuArgs( name="Basic", tier="Standard" ) ) # Create an AKS cluster for deploying model serving applications aks_cluster = azure_native.containerservice.ManagedCluster( 'aksCluster', resource_group_name=resource_group.name, location=ml_workspace.location, agent_pool_profiles=[azure_native.containerservice.ManagedClusterAgentPoolProfileArgs( count=3, vm_size="Standard_DS2_v2", name="agentpool" )], dns_prefix="ai-model-serving-dns" ) # Create an Azure Container Registry container_registry = azure_native.containerregistry.Registry( 'containerRegistry', resource_group_name=resource_group.name, location=ml_workspace.location, sku=azure_native.containerregistry.SkuArgs( name=azure_native.containerregistry.SkuName.STANDARD ), admin_user_enabled=True ) # Exporting the information needed outside of Pulumi pulumi.export('resource_group_name', resource_group.name) pulumi.export('ml_workspace_name', ml_workspace.name) pulumi.export('ml_workspace_url', ml_workspace.workspace_url) pulumi.export('aks_cluster_name', aks_cluster.name) pulumi.export('container_registry_name', container_registry.name) pulumi.export('container_registry_login_server', container_registry.login_server)

    In this script:

    • We create an Azure Resource Group to house the services we'll use.
    • We then set up an Azure Machine Learning workspace, specifying the SKU for the workspace; in this case, we use the "Basic" tier which is a cost-effective option for development and testing.
    • We create an Azure Kubernetes Service cluster with a specified Virtual Machine size and an initial node count of 3 to host our model-serving application.
    • We also set up an Azure Container Registry as a place to store and manage our Docker images required for model serving.

    To use this program, you will need to have the Pulumi CLI installed, have an Azure account configured for Pulumi access, and update the program with your Azure specifics where needed. This program will create the resources once you run it with pulumi up within a directory containing a Pulumi.yaml project file that defines your project name and Python environment.

    Please replace 'ai-model-serving' in the ResourceGroup and dns_prefix in the ManagedCluster with your naming convention to avoid conflicts in Azure.

    This example demonstrates creating fundamental resources for AI model-serving hybrid infrastructure. Depending on complexity, you may need to add networking configurations, security policies, and more specific details surrounding the deployment of your AI models.