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April 11, 2016


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This is all cool, but the tremendous back-and-forth between R and AML lacks simplicity and elegance. For complex machine learning, it would be nice to simply stay in R the entire time: upload data to Azure and then have a powerful engine (no memory constraints, many cores) to run an R script, perhaps also with some API right in R for accessing the Azure tools/module. I notice AML Studio now has some nice features like autocomplete in its R editor... just need the API and you're set. :-)

Hi John - Thanks for your feedback. You can definitely stay in R the entire time. Jupyter Notebooks with R are now a feature of the Azure Machine Learning Studio. You can create and edit R notebooks directly in the Azure ML cloud. Using the AzureML R package (comes installed by default on the Jupyter server), you can interact between R notebooks and your Azure ML environment. For more details see the links below. I would highly recommend checking out the sample R noteboooks in Cortana Intelligence gallery.



This article is very inspiring. I'd love to apply your approach to the problem of predicting costs within the supply chain industry. Can you please contact me or let me know how I can contact you offline to discuss ideas? Thank you.

Chris B.

Hi Chris - Will be happy to discuss. The resources below might be relevant to your industry. Check it out.

A webinar on retail pricing:

Azure ML experiments in Cortana Intelligence gallery used in the tutorial and webinar:

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