Logging in Python SDK

Question

Hi,

I ask whether these differences exist in the Python SDK between logging in training and logging after deployment:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow?view=azureml-api-2

"The Azure Machine Learning Python SDK v2 does not provide native logging or tracking capabilities. This applies not just for logging but also for querying the metrics logged. Instead, use MLflow to manage experiments and runs."

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-collect-production-data?view=azureml-api-2&tabs=azure-cli

"Data collection with custom logging allows you to log pandas DataFrames directly from your scoring script before, during, and after any data transformations. With custom logging, tabular data is logged in real time to your workspace Blob Storage or a custom blob storage container. Your model monitors can consume the data from storage."

Or do I misunderstand sth.?

Bye

Michael

Answer 1

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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