You have an Azure Machine Learning workspace. You plan to run a job to train a model as an MLflow model output. You need to specify the output mode of the MLflow model. Which three modes can you…

The correct answer is A. rw_mount C. upload E. direct. Azure Machine Learning supports three output modes for MLflow model outputs in training jobs: (1) rw_mount - mounts the output storage location as a read-write filesystem path, allowing the job to write directly to mounted storage; (2) upload - the job writes to local storage…

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Question

You have an Azure Machine Learning workspace. You plan to run a job to train a model as an MLflow model output. You need to specify the output mode of the MLflow model. Which three modes can you specify? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

Arw_mount
Bro_mount
Cupload
Ddownload
Edirect

How the community answered

(25 responses)

A
92% (23)
B
4% (1)
D
4% (1)

Explanation

Azure Machine Learning supports three output modes for MLflow model outputs in training jobs: (1) rw_mount - mounts the output storage location as a read-write filesystem path, allowing the job to write directly to mounted storage; (2) upload - the job writes to local storage and the framework uploads the output to the configured datastore after the job completes; (3) direct - the job writes directly to the storage URI without mounting. ro_mount (read-only mount) is an input mode only - outputs cannot be written to a read-only location. download is also strictly an input mode that downloads data from a datastore before job execution. Therefore, the valid output modes are rw_mount, upload, and direct.

Topics

#Azure ML Jobs#MLflow Integration#Output Modes#Model Training

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