You deploy a model as an Azure Machine Learning real-time web service using the following code.
The deployment fails.
You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?
A . service.get_logs()
B . service.state
C . service.serialize()
D . service.update_deployment_state()
Answer: A
Explanation:
You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.
# if you already have the service object handy print(service.get_logs())
# if you only know the name of the service (note there might be multiple services with the same name but different version number)
print(ws.webservices[‘mysvc’].get_logs())
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment
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