You deploy a real-time inference service for a trained model. The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web servi

Question

Exhibit

Options

• AView the explanations for the registered model in Azure ML studio.
• BEnable Azure Application Insights for the service endpoint and view logged data in the Azure
• CView the log files generated by the experiment used to train the model.
• DCreate an ML Flow tracking URI that references the endpoint, and view the data logged by ML

• A
  9% (3)
• B
  83% (29)
• C
  3% (1)
• D
  6% (2)

Explanation

Configure logging with Azure Machine Learning studio You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application 1. Sign in to the studio at https://ml.azure.com. 2. Go to Models and select the model you want to deploy. 3. Select +Deploy. 4. Populate the Deploy model form. 5. Expand the Advanced menu. 6. Select Enable Application Insights diagnostics and data collection. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

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