You create an Azure Databricks workspace and a linked Azure Machine Learning workspace

HOTSPOT

You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.

You have the following Python code segment in the Azure Machine Learning workspace:

import mlflow

import mlflow.azureml

import azureml.mlflow

import azureml.core

from azureml.core import Workspace

subscription_id = ‘subscription_id’

resourse_group = ‘resource_group_name’

workspace_name = ‘workspace_name’

ws = Workspace.get(name=workspace_name,

subscription_id=subscription_id,

resource_group=resource_group)

experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" mlflow.set_experiment(experimentName)

uri = ws.get_mlflow_tracking_uri()

mlflow.set_tracking_uri(uri)

Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

View Answer

Answer:

Explanation:

A screenshot of a computer

Description automatically generated with medium confidence

Box 1: No

The Workspace.get method loads an existing workspace without using configuration files.

ws = Workspace.get(name="myworkspace",

subscription_id='<azure-subscription-id>’,

resource_group=’myresourcegroup’)

Box 2: Yes

MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.

The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.

Box 3: Yes

Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.