Exam DP-100 topic 3 question 156 discussion

Instantiate an object of the MLClient class. In the Azure Machine Learning Python SDK, the MLClient class is used to manage Azure Machine Learning resources. You can use it to access your workspaces, experiments, and other resources. Retrieve the tracking URI of workspace1. The tracking URI is used by MLflow to log metrics and artifacts for a specific experiment. You can get this URI from the workspace object. Set the MLflow tracking URI. MLflow is a platform for managing the machine learning lifecycle. It provides a set of APIs and services that you can use to log and retrieve metrics and artifacts from your machine learning experiments. After getting the tracking URI from the workspace, you should set it as the tracking URI for MLflow.

from azure.ai.ml import MLClient from azure.identity import DefaultAzureCredential # Step 1: Instantiate an object of the MLClient class credential = DefaultAzureCredential() ml_client = MLClient(credential, subscription_id, resource_group, workspace_name) # Step 2: Retrieve the tracking URI of workspace1 tracking_uri = ml_client.workspaces.get(workspace_name).mlflow_tracking_uri # Step 3: Set the MLflow tracking URI import mlflow mlflow.set_tracking_uri(tracking_uri)

this link should provide all the inform you need to answer this question. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2&tabs=python%2Cmlflow

from azure.ai.ml import MLClient from azure.identity import DefaultAzureCredential import mlflow # Define your parameters subscription_id = "<your-subscription-id>" resource_group_name = "<your-resource-group-name>" workspace_name = "<your-workspace-name>" # Instantiate the MLClient class ml_client = MLClient( credential=DefaultAzureCredential(), subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name ) # Retrieve the tracking URI of workspace1 tracking_uri = ml_client.get_tracking_uri() # Set the MLflow tracking URI mlflow.set_tracking_uri(tracking_uri)

chat gtp: Retrieve the tracking URI of workspace1: To interact with the MLflow server in workspace1, you need to retrieve the tracking URI. This can be done using the Azure Machine Learning SDK to access the workspace and retrieve the necessary information. Set the MLflow tracking URI: Once you have the tracking URI of workspace1, you need to set it as the tracking URI for MLflow. This ensures that all metrics and artifacts are logged to the correct MLflow server. Instantiate an object of the MLflow Client class: After setting the tracking URI, you can instantiate an object of the MLflow Client class. This allows you to interact with the MLflow server and log metrics and artifacts from your code.