Empowering AI: Building and Deploying Azure AI Landing Zones with Terraform
Published Aug 04 2023 11:14 AM 22.2K Views
Microsoft

To harness the true potential of AI technologies, like GPT-4, a robust and efficient infrastructure is crucial. Azure Landing Zones provide a structured approach to create a rock-solid cloud environment, while Azure OpenAI Service seamlessly handles AI model deployment and management.

 

Enter Terraform—an Infrastructure as Code (IaC) tool that streamlines the process, ensuring repeatability, consistency, and security. With Terraform, developers and IT teams define the entire AI infrastructure in code, making it simpler to build, modify, and version resources.

In this article, we delve into leveraging Terraform to deploy the Azure OpenAI Landing Zone architecture concentrating on the connectivity and application aspects. By integrating various Azure services and implementing features like Azure Firewall, Virtual Networks, and Private Endpoints, Private DNS Zones, organizations can create a secure and scalable AI environment.

 

We'll also explore the best practices of the Azure Cloud Adoption Framework, a comprehensive guide by Microsoft to ensure successful cloud adoption and alignment with business goals. By following these practices, organizations can accelerate their AI initiatives and maximize the value of Azure services.

 

Join us as we embark on a journey into the world of Azure AI Landing Zones using Terraform, unlocking the true power of AI for your organization.

 

Connectivity

 

The connectivity subscription in Azure Cloud Adoption Framework and Azure Landing Zones is a dedicated Azure subscription used to manage network connectivity resources. It isolates networking components, providing centralized control over configurations, secure connections with on-premises networks, and consistent policies.

 

Using a connectivity subscription benefits AI Services in several ways:

  • Network Isolation: Keeps AI resources separate from other workloads, reducing unauthorized access risks.
  • Centralized Management: Simplifies and ensures consistency in managing network configurations and security policies.
  • Secure Connections: Enables safe communication with on-premises systems using ExpressRoute or VPN Gateways.
  • Scalability and Performance: Optimizes network performance for efficient AI workloads.
  • Compliance and Governance: Helps meet organizational and regulatory requirements for networking and data handling.
  • Easy Resource Tracking: Simplifies tracking and managing network-related costs.

In the article, we will leverage the Azure Landing Zones Terraform module (https://github.com/Azure/terraform-azurerm-caf-enterprise-scale) to quickly deploy crucial connectivity resources like the Hub, Vnets, and Azure Firewall. This module streamlines the creation of platform resources based on Azure landing zones architecture, efficiently building the foundation for your Azure workloads.

 

Target Architecture

 

FreddyAyala_16-1691144303555.png

 

 

In our target architecture, we'll be adopting the hub and spoke topology—a popular networking design in Azure. The hub acts as a central point of connectivity, and each spoke represents a dedicated landing zone or network segment.

 

A spoke, also known as a landing zone, is a focused area that houses specific resources or services. It ensures isolation for improved security and better resource management. Each spoke might correspond to different departments, applications, or environments.

 

To enable smooth communication between the hub and spokes, we'll use network peering. Network peering allows Virtual Networks (VNets) within the same Azure region to communicate privately and securely. This private communication boosts security and performance while simplifying network connectivity.

 

With this hub and spoke setup, we create a scalable and manageable architecture. The hub facilitates seamless data exchange among different spokes, while keeping each spoke isolated for enhanced control and security.

 

This design promotes efficient data flow, reduces complexity, and simplifies network management. As we deploy the Azure Landing Zones using the Terraform module, we'll leverage the hub and spoke topology and network peering to ensure an organized and robust foundation for our AI workloads.

 

Private Endpoints, Private Links and Private DNS Zones

 

In the Azure Cloud Adoption Framework, Private DNS Zones, Private Links, Private Endpoints, and Azure Private DNS Resolver are essential components that enhance security and privacy.

 

Private DNS Zones and Private Endpoints play a crucial role in securing access to services like Azure OpenAI. Private DNS Zones enable custom domain name resolution within your Virtual Network, while Private Endpoints establish private connections to Azure services. By combining these features, you create a strong shield of privacy and security for your AI applications, ensuring data communication remains protected within your private network.

 

In our target architecture, we rely on private endpoints and private links to restrict Azure Open AI access to only within our Landing Zone. This ensures that the service remains internal and secure, allowing integration with other services within a Virtual Network, including on-premises connections through Express Route.

 

To connect to the private endpoint, we need to resolve the hostname of Azure Open AI using a private DNS zone. While the Cloud Adoption Framework recommends deploying this private DNS zone in the connectivity subscription, we've chosen to simplify our code by deploying it alongside Azure Open AI.

 

To enable the hub VNet to resolve the hostname of our private DNS zone, we establish a link between them. Additionally, we can utilize Azure Private Resolver or Azure Firewall Proxy to allow other VNets (configuring the resolver as a custom dns) or on-premises resources to resolve entries in this private DNS zone (using a forwarder).

 

Application Gateway and API Management

 

To securely expose Azure Open AI Services, we use Azure Application Gateway and Azure API Management together.

 

  • Azure Application Gateway:
    • Azure Application Gateway is a powerful layer 7 load balancer that optimizes web traffic in your Azure environment. With its rich set of features, it efficiently directs incoming requests to backend servers, enhancing the performance and availability of your web applications.
    • Additionally, Application Gateway provides SSL termination, decrypting incoming HTTPS requests and forwarding them to backend servers over HTTP. This offloading of SSL decryption from backend servers reduces their computational burden and optimizes their performance.
    • In addition to its load balancing and routing capabilities, Application Gateway comes with a built-in Web Application Firewall (WAF). The WAF provides a robust security layer, protecting your web applications from a range of common web-based attacks, such as SQL injection and cross-site scripting (XSS). It uses rule sets and machine learning to detect and prevent threats, and you can customize rules to meet the specific security needs of your applications.
  • Azure API Management
    • It serves as an API gateway, managing access, authentication, and CORS policies for Azure Open AI Services. and optimized API exposure.
    • Azure API Management acts as a critical gateway, providing granular traffic control to Azure Open AI. Its versatile features include authentication mechanisms like API keys and OAuth2 tokens, ensuring secure access for authorized users. With subscription-based access control, you can create distinct plans, tailoring features and usage limits to different consumer segments.
    • Centralized key management simplifies access control, allowing you to generate, revoke, and monitor API keys seamlessly. By harnessing the power of Azure API Management, you can govern API usage effectively, enforce robust security measures such as rate limiting and throttling, in order to create tailored experiences for your API consumers, optimizing the performance and scalability of your AI applications.
    • Combining these components establishes a secure gateway, controlling access to AI services while providing analytics and monitoring for performance insights. Clients interact with the gateway, which forwards authorized requests to Azure Open AI Services, ensuring a protected

 

Landing Zone Deployment

 

FreddyAyala_19-1691144521543.png

 

 

Using Visual Studio Code and Terraform we will execute the necessary commands to apply the configurations and initiate the deployment process. This approach ensures a structured, consistent, and secure cloud environment, enabling governance and operational success throughout the lifecycle of your AI solutions.

We will deploy the following components:

 

Connectivity Components

  • Azure Virtual Networks (Hub) for secure connectivity to on-premises systems and other spoke networks.
  • Azure Firewall, a network-based, stateful firewall to control and inspect traffic flow in and out of the hub.
  • Bastion, a secure remote desktop access solution for VMs in the virtual network.
  • Jumpbox, a secure jump host to access VMs in private subnets.

AI Workloads:

  • Azure Open AI, a managed AI service for running advanced language models like GPT-4.
  • Create Spoke Networks, separate virtual networks for securely hosting AI workloads.
  • Subnets within spoke networks to isolate different components.
  • Route Tables for controlling traffic flow within virtual networks.
  • Application Gateway, a load balancer for secure access to web applications and AI services.
  • Azure API Management as the API gateway for managing and securing APIs, including Azure Open AI.
  • Private DNS Zones for name resolution within the virtual network and between VNets.
  • Cosmos DB, a globally distributed, multi-model database service to support AI applications.
  • Web applications in Azure Web App.
  • Azure AI services for building intelligent applications.

Deployment Steps

 

  1. Clone the GitHub Repository (https://github.com/FreddyAyala/AzureAIServicesLandingZone)

Download the source code from the GitHub repository containing the Terraform configurations for the Landing Zone and AI workloads.

 

  1. Deploy Landing Zone:

In the /Landing_Zones/ folder, create a file named terraform.tfvars and set the connectivity_subscription variable to your connectivity subscription ID.

Modify the settings in Landing_Zone\settings.connectivity.tf according to your requirements.

FreddyAyala_0-1691145128939.png

 

 

Authenticate to Azure using the Azure CLI by running az login in your command-line interface.

  1. Initialize Terraform Modules:

Navigate to the /Landing_Zone folder in your command-line interface.

Run terraform init -reconfigure to initialize your Terraform repository, using the local state.

FreddyAyala_1-1691145138825.png

 

 

  1. Preview Your Deployment:

FreddyAyala_2-1691145147845.png

 

 

Run terraform plan -var-file="terraform.tfvars" to preview the resources that will be created based on your configurations.

  1. Deploy Connectivity Infrastructure:

Execute terraform apply -var-file="terraform.tfvars" to deploy the connectivity infrastructure for the Landing Zone.

Once your landing zone has been deployed you can go to the Azure Portal to verify the components that have been deployed:

 

FreddyAyala_3-1691145163239.png

FreddyAyala_7-1691145212619.png

 

  1. Deploy AI Workloads:

Go into the /Workload/AI folder.

Create a file named terraform.tfvars and set the connectivity_subscription, ai_subscription, and hub_vnet_id variables to their respective values.

Use the same steps as above (3 and 4) to initialize Terraform modules and preview the AI workload deployment.

Execute terraform apply -var-file="terraform.tfvars" to deploy the AI workloads.

FreddyAyala_6-1691145197884.png

You can also see your deployed network topology:

 

FreddyAyala_8-1691145233537.png

 

  1. Configure Azure API Management (APIM):

 

Use the provided policy to test OpenAI API behind APIM.

Insert your OpenAI API key and backend service URL into the policy to enable access to the AI services.

You can use the following Policy to test OpenAI API behind APIM

 

 

<!--

    IMPORTANT:

    - Policy elements can appear only within the <inbound>, <outbound>, <backend> section elements.

    - To apply a policy to the incoming request (before it is forwarded to the backend service), place a corresponding policy element within the <inbound> section element.

    - To apply a policy to the outgoing response (before it is sent back to the caller), place a corresponding policy element within the <outbound> section element.

    - To add a policy, place the cursor at the desired insertion point and select a policy from the sidebar.

    - To remove a policy, delete the corresponding policy statement from the policy document.

    - Position the <base> element within a section element to inherit all policies from the corresponding section element in the enclosing scope.

    - Remove the <base> element to prevent inheriting policies from the corresponding section element in the enclosing scope.

    - Policies are applied in the order of their appearance, from the top down.

    - Comments within policy elements are not supported and may disappear. Place your comments between policy elements or at a higher level scope.

-->

<policies>

    <inbound>

        <base />

        <set-header name="api-key" exists-action="override">

            <value> <!-- Add Your OpenAI API Key --></></value>

        </set-header>

        <set-header name="Content-Type" exists-action="override">

            <value>application/json</value>

        </set-header>        

        <set-backend-service base-url="https://<!-- Your OpenAI Backend Service -->.privatelink.openai.azure.com" />

    </inbound>

    <backend>

        <forward-request timeout="5" />

    </backend>

    <outbound>

        <!-- Add a policy to capture and return the full response -->

        <base />

        <return-response>

            <set-status code="200" />

            <set-header name="Content-Type" exists-action="override">

                <value>application/json</value>

            </set-header>

            <set-body>@(context.Response.Body.As<string>())</set-body>

        </return-response>

    </outbound>

    <on-error />

</policies>

 

 

 

Test you Azure AI Landing Zone

 

With the use of private endpoints, direct access to Azure Open AI is restricted to your internal network, ensuring a secure environment. To interact with Azure Open AI services from external networks or locations, we can set up Azure Bastion.

 

Azure Bastion provides a secure and seamless way to connect to virtual machines and resources within your virtual network. It acts as a jumpbox or a gateway that allows you to access VMs without exposing them to the public internet. This enhances the security of your infrastructure and reduces the attack surface.

 

To connect to Azure Open AI from external networks, you can use Azure Bastion to access a jumpbox within your virtual network. The jumpbox acts as an intermediary where you can execute requests to Azure Open AI services securely. This way, you can manage and interact with Azure Open AI while maintaining a robust security posture.

 

By leveraging Azure Bastion and private endpoints, you can strike a balance between seamless accessibility and stringent security measures, making your AI infrastructure in Azure a safe and efficient environment for advanced language models.

You can connect into your deployed jumpbox vm using Azure bastion:

 

FreddyAyala_9-1691145295322.png

 

And from the jumpbox you can query your APIM or Azure OpenAI service:

 

 

$SubscriptionKey = "<your subscription key>"

$headers = @{

    "Content-Type" = "application/json"

    "Ocp-Apim-Subscription-Key" = $SubscriptionKey

    "Ocp-Apim-Trace"="true"

}



$uri = "http://20.123.161.172/test"



$body = @{

    "prompt" = "Once upon a time"

    "max_tokens" = 5

} | ConvertTo-Json



Invoke-WebRequest -Uri $uri -Headers $headers -Method POST -Body $body 

 

 

 

FreddyAyala_0-1691146034634.png

 

Furthermore, since you might want to query your Azure OpenAI from an external network you can leverage your Azure App Gateway as well.

FreddyAyala_11-1691145315136.png

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You can use the Public IP of the Azure APP Gateway to reach in a secure way your Azure Open AI API, APIM or other custom web applications that might use Azure OpenAI or Azure AI services.

 

Take into account that in the example code provided, there are several opportunities for further improvement and additional features that can be implemented to enhance the Azure AI Landing Zone:

 

  • Managed Identity: Implementing managed identity for authenticating with Azure services can eliminate the need to use Azure OpenAI keys directly, improving security and simplifying access management.
  • Azure Key Vault: Integrating Azure Key Vault with a private endpoint can provide a more secure and centralized approach to managing secrets, keys, and certificates, ensuring better protection of sensitive information.
  • SSL/TLS at App Gateway: Enabling SSL/TLS certificates at the Azure Application Gateway level will enhance data encryption and security, ensuring that communication between clients and web applications/AI services is protected.
  • Azure Private DNS Resolver: Deploying Azure Private DNS Resolver will facilitate seamless DNS resolution between virtual networks, enhancing communication and reducing reliance on public DNS servers.
  • Azure Firewall Policies and Dashboards: Implementing Azure Firewall policies and dashboards will enable better control and monitoring of network traffic, bolstering the overall security posture of the AI Landing Zone.
  • Naming Conventions and Tags: Introducing consistent naming conventions and resource tagging will improve resource organization and simplify management, leading to better governance and visibility.
  • Azure Monitor and Logging: Implementing Azure Monitor and logging solutions will provide insights into resource performance, health, and usage, enabling proactive management and issue resolution.
  • Azure Policy: Enforcing Azure Policy to enforce compliance and governance rules will help maintain a standardized and secure environment across all resources.
  • Advanced Networking Configurations: Explore advanced networking configurations, such as Azure Virtual WAN or ExpressRoute, to optimize network performance and connectivity.
  • High Availability and Disaster Recovery: Implementing high availability and disaster recovery solutions will ensure business continuity and data protection in case of unexpected events.
  • Azure Front Door: Utilizing Azure Front Door for global load balancing, SSL offloading, and enhanced security features can improve the overall performance and reliability of web applications.
  • Catching and Integration with other services: Using Azure Cognitive Search, using caching with Redis.

 

Additionally, you can explore integrating Azure ChatGPT, an open-source application, into your Azure AI Landing Zone. Azure ChatGPT provides a user-friendly interface to interact with AI language models like GPT-4. With this application, users can input prompts and receive AI-generated responses in a conversational manner.

 

Conclusion

 

To harness the true potential of AI technologies, a robust infrastructure is crucial. Azure Landing Zones provide a structured approach to creating a rock-solid cloud environment, while Azure OpenAI Service handles AI model deployment and management.

Using Terraform, developers and IT teams define the entire AI infrastructure in code, making it simpler to build, modify, and version resources.

 

In this article, we delved into leveraging Terraform to deploy the Azure OpenAI Landing Zone architecture, focusing on connectivity and applications. By integrating Azure services and features like Azure Firewall, Virtual Networks, Private Endpoints, and Private DNS Zones, organizations can create a secure and scalable AI environment.

By following the best practices of the Azure Cloud Adoption Framework, organizations can accelerate AI initiatives and maximize the value of Azure services.

 

To deploy the Landing Zone and AI workloads using Terraform, follow the steps provided. This creates a structured and secure cloud environment, enabling governance and operational success for your AI solutions. However, be aware that this code is just an example, might not be production ready and there are many things to improve.

Nevertheless, by using this foundation, you can unleash the full potential of advanced language models like GPT-4, empowering your organization with cutting-edge AI capabilities

 

           

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