Use an Azure AI Services Container

Using Azure AI services hosted in Azure enables application developers to focus on the infrastructure for their own code while benefiting from scalable services that are managed by Microsoft. However, in many scenarios, organizations require more control over their service infrastructure and the data that is passed between services.

Many of the Azure AI services APIs can be packaged and deployed in a container, enabling organizations to host Azure AI services in their own infrastructure; for example in local Docker servers, Azure Container Instances, or Azure Kubernetes Services clusters. Containerized Azure AI services need to communicate with an Azure-based Azure AI services account to support billing; but application data is not passed to the back-end service, and organizations have greater control over the deployment configuration of their containers, enabling custom solutions for authentication, scalability, and other considerations.

Note: There is an issue currently being investigated that some users hit where containers won’t deploy properly, and calls to those containers fail. Updates to this lab will be made as soon as the issue has been resolved.

Clone the repository in Visual Studio Code

You’ll develop your code using Visual Studio Code. The code files for your app have been provided in a GitHub repo.

Tip: If you have already cloned the mslearn-ai-services repo, open it in Visual Studio code. Otherwise, follow these steps to clone it to your development environment.

  1. Start Visual Studio Code.
  2. Open the palette (SHIFT+CTRL+P) and run a Git: Clone command to clone the repository to a local folder (it doesn’t matter which folder).
  3. When the repository has been cloned, open the folder in Visual Studio Code.
  4. Wait while additional files are installed to support the C# code projects in the repo, if necessary

    Note: If you are prompted to add required assets to build and debug, select Not Now.

  5. Expand the Labfiles/04-use-a-container folder.

Provision an Azure AI Services resource

If you don’t already have one in your subscription, you’ll need to provision an Azure AI Services resource.

  1. Open the Azure portal at, and sign in using the Microsoft account associated with your Azure subscription.
  2. In the top search bar, search for Azure AI services, select Azure AI Services, and create an Azure AI services multi-service account resource with the following settings:
    • Subscription: Your Azure subscription
    • Resource group: Choose or create a resource group (if you are using a restricted subscription, you may not have permission to create a new resource group - use the one provided)
    • Region: Choose any available region
    • Name: Enter a unique name
    • Pricing tier: Standard S0
  3. Select the required checkboxes and create the resource.
  4. Wait for deployment to complete, and then view the deployment details.
  5. When the resource has been deployed, go to it and view its Keys and Endpoint page. You will need the endpoint and one of the keys from this page in the next procedure.

Deploy and run a Text Analytics container

Many commonly used Azure AI services APIs are available in container images. For a full list, check out the Azure AI services documentation. In this exercise, you’ll use the container image for the Text Analytics language detection API; but the principles are the same for all of the available images.

  1. In the Azure portal, on the Home page, select the +Create a resource button, search for container instances, and create a Container Instances resource with the following settings:

    • Basics:
      • Subscription: Your Azure subscription
      • Resource group: Choose the resource group containing your Azure AI services resource
      • Container name: Enter a unique name
      • Region: Choose any available region
      • Image source: Other Registry
      • Image type: Public
      • Image:
      • OS type: Linux
      • Size: 1 vcpu, 12 GB memory
    • Networking:
      • Networking type: Public
      • DNS name label: Enter a unique name for the container endpoint
      • Ports: Change the TCP port from 80 to 5000
    • Advanced:
      • Restart policy: On failure
      • Environment variables:

        Mark as secure Key Value
        Yes ApiKey Either key for your Azure AI services resource
        Yes Billing The endpoint URI for your Azure AI services resource
        No Eula accept
      • Command override: [ ]
    • Tags:
      • Don’t add any tags
  2. Select Review + create then select Create. Wait for deployment to complete, and then go to the deployed resource.

    Note Please note that deploying an Azure AI container to Azure Container Instances typically takes 5-10 minutes (provisioning) before they are ready to use.

  3. Observe the following properties of your container instance resource on its Overview page:
    • Status: This should be Running.
    • IP Address: This is the public IP address you can use to access your container instances.
    • FQDN: This is the fully-qualified domain name of the container instances resource, you can use this to access the container instances instead of the IP address.

    Note: In this exercise, you’ve deployed the Azure AI services container image for text translation to an Azure Container Instances (ACI) resource. You can use a similar approach to deploy it to a Docker host on your own computer or network by running the following command (on a single line) to deploy the language detection container to your local Docker instance, replacing <yourEndpoint> and <yourKey> with your endpoint URI and either of the keys for your Azure AI services resource. The command will look for the image on your local machine, and if it doesn’t find it there it will pull it from the image registry and deploy it to your Docker instance. When deployment is complete, the container will start and listen for incoming requests on port 5000.

     docker run --rm -it -p 5000:5000 --memory 12g --cpus 1 Eula=accept Billing=<yourEndpoint> ApiKey=<yourKey>

Use the container

  1. In your editor, open rest-test.cmd and edit the curl command it contains (shown below), replacing <your_ACI_IP_address_or_FQDN> with the IP address or FQDN for your container.

     curl -X POST "http://<your_ACI_IP_address_or_FQDN>:5000/text/analytics/v3.0/languages" -H "Content-Type: application/json" --data-ascii "{'documents':[{'id':1,'text':'Hello world.'},{'id':2,'text':'Salut tout le monde.'}]}"
  2. Save your changes to the script by pressing CTRL+S. Note that you do not need to specify the Azure AI services endpoint or key - the request is processed by the containerized service. The container in turn communicates periodically with the service in Azure to report usage for billing, but does not send request data.
  3. Enter the following command to run the script:

  4. Verify that the command returns a JSON document containing information about the language detected in the two input documents (which should be English and French).

Clean Up

If you’ve finished experimenting with your container instance, you should delete it.

  1. In the Azure portal, open the resource group where you created your resources for this exercise.
  2. Select the container instance resource and delete it.

Clean up resources

If you’re not using the Azure resources created in this lab for other training modules, you can delete them to avoid incurring further charges.

  1. Open the Azure portal at, and in the top search bar, search for the resources you created in this lab.

  2. On the resource page, select Delete and follow the instructions to delete the resource. Alternatively, you can delete the entire resource group to clean up all resources at the same time.

More information

For more information about containerizing Azure AI services, see the Azure AI Services containers documentation.