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


This article shows how to use the CLI and SDK v1 to deploy a model. For the recommended approach for v2, see Deploy and score a machine learning model by using an online endpoint.

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.


It is highly advised to debug locally before deploying to the web service, for more information, see Debug Locally

You can also refer to Azure Machine Learning - Deploy to Local Notebook



When your Azure Machine Learning workspace is configured with a private endpoint, deploying to Azure Container Instances in a virtual network isn't supported. Instead, consider using a Managed online endpoint with network isolation.

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.


  • ACI is suitable only for small models that are under 1 GB in size.
  • We recommend using single-node AKS to dev-test larger models.
  • The number of models to be deployed is limited to 1,000 models per deployment (per container).

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)

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

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:

JSON entity Method parameter Description
computeType NA The compute target. For ACI, the value must be ACI.
containerResourceRequirements NA Container for the CPU and memory entities.
  cpu cpu_cores The number of CPU cores to allocate. Defaults, 0.1
  memoryInGB memory_gb The amount of memory (in GB) to allocate for this web service. Default, 0.5
location location The Azure region to deploy this Webservice to. If not specified the Workspace location will be used. More details on available regions can be found here: ACI Regions
authEnabled auth_enabled Whether to enable auth for this Webservice. Defaults to False
sslEnabled ssl_enabled Whether to enable TLS for this Webservice. Defaults to False.
appInsightsEnabled enable_app_insights Whether to enable AppInsights for this Webservice. Defaults to False
sslCertificate ssl_cert_pem_file The cert file needed if TLS is enabled
sslKey ssl_key_pem_file The key file needed if TLS is enabled
cname ssl_cname The CNAME for if TLS is enabled
dnsNameLabel dns_name_label The dns name label for the scoring endpoint. If not specified a unique dns name label will be generated for the scoring endpoint.

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

    "computeType": "aci",
        "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.


You don't need to create an ACI container to test in advance. ACI containers are created as needed.


We append hashed workspace id to all underlying ACI resources which are created, all ACI names from same workspace will have same suffix. The Azure Machine Learning service name would still be the same customer provided "service_name" and all the user facing Azure Machine Learning SDK APIs do not need any change. We do not give any guarantees on the names of underlying resources being created.

Next steps