Azure Machine Learning Studio vs. Workbench

Question

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:

• Studio has a hard limit of 10 GB total input of training data per module, whereas Workbench has a variable limit by price.
• 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 offering should I prefer?

Answer 1

It should be added that Azure Machine Learning Workbench is deprecated since september 2018 and has been replaced by the Azure Machine Learning services, which was made generally available in december 2018. The core functionality is still intact, but some major changes to point out about the architecture are:

• A simplified Azure resources model
• New portal UI to manage your experiments and compute targets
• A new, more comprehensive Python SDK
• A new expanded Azure CLI extension for machine learning