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Azure ML Thursday 7: xgboost in Azure ML Studio

Last week, we trained an xgboost model for our dataset inside R. In order to use your trained dataset in Azure ML, you need to export & upload it much like we did two weeks ago in Python. Today, I’ll show how to import the trained R model into Azure ML studio, thus enabling you[...]
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Azure ML Thursday 6: xgboost in R

Last Azure ML Thursdays we explored how to do our Machine Learning in Python. Python in Azure ML doesn’t include one particularly succesful algorithm though – xgboost. Python packages are available, but just not yet for Windows – which means also not inside Azure ML Studio. But they are available inside R! Today, we take the same approach[...]
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Azure ML Thursday 5: trained Python models

Last week, we stepped out of Azure ML to look at building ML models in Python using scikit-learn. Today, we focus on getting the trained model back into Azure ML – the place where my ML solutions live in a managed, enterprise environment.
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Azure ML Thursday 4: ML in Python

On this fourth Azure ML Thursday series we move our ML solution out of Azure ML and set our first steps in Python with scikit-learn. Today, we look at using “just” Python for doing ML, next week we bring the trained models to Azure ML. You’ll notice there’s a lot more to tweak and improve once you do[...]
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Moving BI to the cloud: Consider this…

While looking into the possibilities of moving my customer’s BI infrastructure into the cloud, my primary question was how to handle the lack of cloud SSAS. But there’s a lot more to keep in mind. Here’s a few things that I think should be thought of from an architectural point of view: not only in[...]
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Moving BI to the Cloud: Ditch SSAS?

Some time ago I read Chris Webb blogged “Thoughts On The Power BI Announcements At The MS Data Insights Summit“, where between the lines was this rather interesting point: For the last few years my customers have asked me when MS was going to release SSAS in the cloud and I’ve always replied that Power[...]
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