Which four actions should you perform in sequence?

DRAG DROP

You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.

When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.

You need to debug the service on the local workstation before deploying the service to production.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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Answer:

Explanation:

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Description automatically generated

Step 1: Install Docker on the workstation

Prerequisites include having a working Docker installation on your local system.

Build or download the dockerfile to the compute node.

Step 2: Create an AksWebservice deployment configuration and deploy the model to it To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.

# 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)

service.wait_for_deployment(show_output = True)

print(service.state)

print(service.get_logs())

Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it

To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.

Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.

During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you’ve discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.