Deploy a model to Azure Container Instances with CLI (v1)

Learn how to use Azure Machine Learning to deploy a model as a web service on Azure Container Instances (ACI). Use Azure Container Instances if you:

• prefer not to manage your own Kubernetes cluster
• Are OK with having only a single replica of your service, which might affect uptime

For information on quota and region availability for ACI, see Quotas and region availability for Azure Container Instances article.

Prerequisites

• An Azure Machine Learning workspace. For more information, see Create an Azure Machine Learning workspace.

• A machine learning model registered in your workspace. If you don't have a registered model, see How and where to deploy models.

• The Azure CLI extension (v1) for Machine Learning service, Azure Machine Learning Python SDK, or the Azure Machine Learning Visual Studio Code extension.

  Important

  Some of the Azure CLI commands in this article use the azure-cli-ml, or v1, extension for Azure Machine Learning. Support for the v1 extension will end on September 30, 2025. You will be able to install and use the v1 extension until that date.

  We recommend that you transition to the ml, or v2, extension before September 30, 2025. For more information on the v2 extension, see Azure ML CLI extension and Python SDK v2.

• The Python code snippets in this article assume that the following variables are set:

  • ws - Set to your workspace.
  • model - Set to your registered model.
  • inference_config - Set to the inference configuration for the model.

  For more information on setting these variables, see How and where to deploy models.

• The CLI snippets in this article assume that you've created an inferenceconfig.json document. For more information on creating this document, see How and where to deploy models.

Limitations

Deploy to ACI

To deploy a model to Azure Container Instances, create a deployment configuration that describes the compute resources needed. For example, number of cores and memory. You also need an inference configuration, which describes the environment needed to host the model and web service. For more information on creating the inference configuration, see How and where to deploy models.

Using the SDK

APPLIES TO: Python SDK azureml v1

from azureml.core.webservice import AciWebservice, Webservice
from azureml.core.model import Model

deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)
service = Model.deploy(ws, "aciservice", [model], inference_config, deployment_config)
service.wait_for_deployment(show_output = True)
print(service.state)

For more information on the classes, methods, and parameters used in this example, see the following reference documents:

• AciWebservice.deploy_configuration
• Model.deploy
• Webservice.wait_for_deployment

Using the Azure CLI

APPLIES TO: Azure CLI ml extension v1

To deploy using the CLI, use the following command. Replace mymodel:1 with the name and version of the registered model. Replace myservice with the name to give this service:

az ml model deploy -n myservice -m mymodel:1 --ic inferenceconfig.json --dc deploymentconfig.json

The entries in the deploymentconfig.json document map to the parameters for AciWebservice.deploy_configuration. The following table describes the mapping between the entities in the JSON document and the parameters for the method:

The following JSON is an example deployment configuration for use with the CLI:

{
    "computeType": "aci",
    "containerResourceRequirements":
    {
        "cpu": 0.5,
        "memoryInGB": 1.0
    },
    "authEnabled": true,
    "sslEnabled": false,
    "appInsightsEnabled": false
}

For more information, see the az ml model deploy reference.

Using VS Code

See how to manage resources in VS Code.

Next steps

• How to deploy a model using a custom Docker image
• Deployment troubleshooting
• Update the web service
• Use TLS to secure a web service through Azure Machine Learning
• Consume a ML Model deployed as a web service
• Monitor your Azure Machine Learning models with Application Insights
• Collect data for models in production