Logging in Python SDK

Hi Michael Schmidt,

Welcome to Microsoft Q&A Forum, thank you for posting your query here!

The Azure Machine Learning Python SDK v2 does not provide built-in logging capabilities for training or post-deployment scenarios. Instead:

During training, MLflow is used to log models, metrics, parameters, and artifacts. This allows you to effectively track experiments, compare results, and manage different runs.

After deployment, custom logging can be implemented to capture model input and output data. This helps monitor model behavior and performance.

Although the approaches are different, both methods play a crucial role in tracking, analyzing, and improving the overall performance of machine learning models.

You can consider document as reference for Log metrics, parameters, and files with MLflow.

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