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 we

The correct answer is A. service.get_logs(). 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 # if you already have the service object handy print(service.get_logs()) # if you only know

Train and deploy models

Question

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?

Options

Aservice.get_logs()
Bservice.state
Cservice.serialize()
Dservice.update_deployment_state()

How the community answered

(19 responses)

A
89% (17)
B
5% (1)
D
5% (1)

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 # 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()) https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

Topics

#Azure Machine Learning#Model Deployment#Troubleshooting#Deployment Logs

Community Discussion

No community discussion yet for this question.

Full DP-100 Practice