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Question 415

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

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You have an Azure Machine Learning workspace named Workspace1. Workspace1 has a registered MLflow model named model1 with PyFunc flavor.

You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.

You have the following code:

You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.

Solution: Add the with_package parameter.

Does the solution meet the goal?

    Correct Answer:

    To ensure the model deploys successfully to an online endpoint in Azure Machine Learning without egress connectivity, the 'with_package' parameter is not necessarily relevant within the ManagedOnlineDeployment object itself. Instead, you need to ensure that the essential parameters such as 'model', 'code_configuration', and 'environment' are appropriately set at the time of deployment. The 'with_package' flag can be used in command-line interface (CLI) integration but is not directly applicable in the Python SDK's ManagedOnlineDeployment object. Therefore, adding 'with_package' to the given code does not meet the goal.

Discussion
cryodaxOption: B

The ManagedOnlineDeployment class requires the following parameters: name: str: Name of the deployment resource. model: str | Model | None: Model entity for the endpoint deployment, defaults to None. code_configuration: CodeConfiguration | None: Code Configuration, defaults to None. environment: str | Environment | None: Environment entity for the endpoint deployment, defaults to None. These are the minimum required parameters to create an instance of the ManagedOnlineDeployment class. All other parameters are optional and have default values. Please note that while model, code_configuration, and environment are optional in the constructor, they are typically necessary for a successful deployment. If not provided in the constructor, they should be set before deployment.

cryodaxOption: B

B. No Scoring script is a requirement for managedonlinedeployment

f2a9aa5Option: A

A. To deploy an MLflow model to an online endpoint in Azure Machine Learning without egress connectivity, you can use model packaging. Here’s how: First, ensure that your workspace has no public network access. Package your MLflow model using the --with-package flag: az ml online-deployment create --with-package --endpoint-name $ENDPOINT_NAME -f blue-deployment.yml --all-traffic Replace $ENDPOINT_NAME with your desired endpoint name. This approach allows you to avoid the need for an internet connection while deploying MLflow models. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=cli

cryodaxOption: B

Sorry, scoring_script is optional. Copilot response: When using the ManagedOnlineDeployment class with the pyfunc flavor, the parameters remain the same. However, the model and code_configuration parameters are particularly important: model: str | Model | None: This should be an instance of mlflow.pyfunc.PyFuncModel or the URI of the model saved with mlflow.pyfunc.save_model1. code_configuration: CodeConfiguration | None: This should be an instance of CodeConfiguration where the code_flavor is set to pyfunc.