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What is the difference between Azure Machine Learning Studio and Azure Machine Learning Workbench? What is the intended difference? And is it expected that Workbench is heading towards deprecation in favor of Studio?

I have gathered an assorted collection of differences:

  • The studio has a hard limit of 10 GB total input of training data per module, whereas Workbench has a variable limit by price.
  • The studio appears to have a more fully-featured GUI and user-friendly deployment tools, whereas Workbench appears to have more powerful/customizable deployment tools.
  • etc.

However, I have also found several scattered references claiming that Studio is a renamed updated of Workbench, even though both services appear to still be offered.

For a fresh Data Scientist looking to adopt the Microsoft stack (potentially on an enterprise-scale within the medium-term and for the long-term), which offer should I prefer?

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They are not the same. Azure ML Workbench is part of Azure ML Services and not Studio. To know more about the differences between these two, refer to this blog: click here

Instead of Workbench, you can work on Workspace in Azure Portal. 

If you like to code your ML models, Azure ML Workspaces will be a great place to work.

If you want simpler things to build your model, you can work with Studio. Though, studio is preferred by people who are well-versed with ML.   

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