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We are trying to use Support Vector Machines to do predictions on our dataset but with just 70,000 rows and 7 features - we have tried an SVM on Google DataLabs but our data set is too big to calculate in any reasonable finite time on the DataLabs VM.

We would like to leverage an approach that scales statistical approaches across CPU cores like Revolution Analytics version of R on Azure Machine Learning Studio but our data is on Google BigQuery.

How do we connect an R script on Azure Machine Learning Studio to use our dataset on Google BigQuery?

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Here is an article to help you export your data from BigQuery. To create a connection, you can also use R console. Otherwise, you can write a python script in Execute Python Script model using a google sdk for python and then include R script in it.

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