How should you complete the code?

HOTSPOT

You use Azure Machine Learning to train and register a model.

You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.

Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.

You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Answer:

Explanation:

Box 1: AksCompute

Example:

aks_target = AksCompute(ws,"myaks")

# If deploying to a cluster configured for dev/test, ensure that it was created with enough

# cores and memory to handle this deployment configuration. Note that memory is also used by

# things such as dependencies and AML components.

deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)

service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target)

Box 2: AksWebservice

Box 3: token_auth_enabled=Yes

Whether or not token auth is enabled for the Webservice.

Note: A Service principal defined in Azure Active Directory (Azure AD) can act as a principal on which authentication and authorization policies can be enforced in Azure Databricks.

The Azure Active Directory Authentication Library (ADAL) can be used to programmatically get an Azure AD access token for a user.

Incorrect Answers:

auth_enabled (bool): Whether or not to enable key auth for this Webservice. Defaults to True.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service

https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token