You have an Azure Machine Learning workspace named WS1. You plan to use the Responsible AI dashboard to assess MLflow models that you will register in WS1. You need to identify the library you…

The correct answer is D. scikit-learn. The Azure Machine Learning Responsible AI dashboard has its broadest and most stable support for models built and registered with scikit-learn (via 'mlflow.sklearn'). The dashboard components - such as model explanations, error analysis, and data exploration - integrate…

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Question

You have an Azure Machine Learning workspace named WS1. You plan to use the Responsible AI dashboard to assess MLflow models that you will register in WS1. You need to identify the library you should use to register the MLflow models. Which library should you use?

Options

APyTorch
Bmlpy
CTensorFlow
Dscikit-learn

How the community answered

(33 responses)

A
6% (2)
B
12% (4)
C
3% (1)
D
79% (26)

Explanation

The Azure Machine Learning Responsible AI dashboard has its broadest and most stable support for models built and registered with scikit-learn (via 'mlflow.sklearn'). The dashboard components - such as model explanations, error analysis, and data exploration - integrate natively with scikit-learn estimators. While PyTorch and TensorFlow have MLflow autolog support, the Responsible AI tooling in Azure ML is primarily designed and validated around scikit-learn MLflow models. 'mlpy' is an obscure third-party library with no special Azure ML integration.

Topics

#MLflow#Responsible AI#Model Registration#scikit-learn

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