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azure-machine-learning

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This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

  • Updated Jun 21, 2022
  • Python
revodavid
revodavid commented Jan 26, 2020

None of the function help pages in the azuremlsdk library include a See Also section. Including relevant functions in a See Also section would help users discover functions and navigate the help system better.

Describe the solution you'd like
Add a see also section to the help pages listing relevant functions. For example, upload_files_to_datastore should refer to `download_from_datastor

dstoolkit-mlops-base
FlorianPydde
FlorianPydde commented Nov 3, 2021

AML provides a model profiling functionality which enables team to assess their deployment services (memory consumption, latency, etc): https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-profile-model?pivots=py-sdk

During model deployment to AKS (TEST/PROD), it may be useful to create the model profile and upload it to the default blob storage, for simplicity. This file can u

enhancement good first issue
smletsexchangeconnector

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