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Microsoft

DP-100 · Question #430

You create an Azure Machine Learning workspace named workspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local…

The correct answer is D. Configure the tracking URL. When moving an MLflow-tracked notebook from a local computer to an Azure ML compute instance, the MLflow tracking URI must be configured to point to the Azure ML workspace so that metrics and artifacts are logged correctly.

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Question

You create an Azure Machine Learning workspace named workspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer. You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1. You need to continue to log metrics and artifacts from your data science code. What should you do?

Options

  • AInstantiate the job class.
  • BInstantiate the MLCIient class.
  • CLog in to workspace1.
  • DConfigure the tracking URL.

How the community answered

(45 responses)
  • A
    16% (7)
  • B
    4% (2)
  • C
    7% (3)
  • D
    73% (33)

Why each option

When moving an MLflow-tracked notebook from a local computer to an Azure ML compute instance, the MLflow tracking URI must be configured to point to the Azure ML workspace so that metrics and artifacts are logged correctly.

AInstantiate the job class.

The job class is used to define and submit Azure ML jobs programmatically, not to configure metric logging for a notebook running interactively on a compute instance.

BInstantiate the MLCIient class.

MLClient is the Azure ML SDK v2 management client used to interact with workspace resources; it does not configure MLflow tracking behavior.

CLog in to workspace1.

Logging in to the workspace authenticates the user but does not redirect MLflow's tracking destination from local storage to the Azure ML workspace.

DConfigure the tracking URL.Correct

MLflow uses a tracking URI to determine where to send logged metrics and artifacts. On a local machine the URI defaults to a local directory, but when running on an Azure ML compute instance the URI must be set to the Azure ML workspace's MLflow tracking endpoint. Configuring this URL redirects all mlflow.log_metric() and mlflow.log_artifact() calls to the workspace's experiment store without requiring changes to the data science code itself.

Concept tested: Configuring MLflow tracking URI for Azure ML compute

Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs

Topics

#MLflow#Experiment Tracking#Azure Machine Learning#Compute Instance

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