Power BI Embedded: Step 3 - integrate your report using PHP and JavaScript

Using Power BI Embedded you can integrate your report in a website or web-application. To do so there are three parts you need to do:

  • Configure your Azure environment
  • Add a Power BI workspace to this environment and upload a report
  • Implement the code to generate a token and embed your report

In this last post we will zoom in on the third part. In the previous post we’ve created a workspace, uploaded our report and saved the ID’s of the workspace and the report. In this post we will use that information (in combination with the Access Key) too generate a token and embed the report.

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Power BI Embedded: Step 2 - Create a workspace with Node.js and add a report

Using Power BI Embedded you can integrate your report in a website or web-application. To do so there are three parts you need to do:

  • Configure your Azure environment
  • Add a Power BI workspace to this environment and upload a report
  • Implement the code to generate a token and embed your report

In this post we will zoom in on the second part. In the previous post we’ve created a Power BI Workspace Collection in Azure. We will use the Access Key of this collection to add a workspace and upload our .PBIX Power BI report.

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Power BI Embedded: Step 1 - Configuring Azure

Using Power BI Embedded you can integrate your report in a website or web-application. To do so there are three parts you need to do:

  • Configure your Azure environment
  • Add a Power BI workspace to this environment and upload a report
  • Implement the code to generate a token and embed your report

In this post we will zoom in on the first part: setting up your Azure environment.

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Run Azure Stream Analytics job from UWP app

 

Working with the newest technologies is great and frustrating. I can create solutions I’ve never made before while complaining about the outdated/incomplete documentation.

I had one of those feelings while working with Azure Stream Analytics (ASA). My solution worked but there was one ‘elementary and simple’ thing I wanted: Start the ASA-jobs within my C#-code. That shouldn’t be hard and there’s some documentation. But no, I needed to combine several opposed solutions to a new one to make it possible. Continue reading...

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 to use xgboost in Azure ML studio. If you combine last week's knowledge of using xgboost with today's knowledge of importing trained xgboost models inside Azure ML Studio, it's not too hard to climb the leaderboards of the (still ongoing) WHRA challenge!

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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 as two weeks ago: first, we move out of Azure ML to do our first ML in R, then (next week) we'll upload and use our trained R model inside Azure ML studio.

Today, I'll show you how use xgboost on the still ongoing Cortana Intelligence Competition "Women's Health Risk Assessment" (WHRA). At the moment of writing, the leaderboard stayed the same for over three weeks, with only 336 participants - but ending in a week, with a grand prize of $3,000.

So rush to participate, and use the knowledge shared here to win - all code presented below can be run in order and will result in a trained model for the WHRA dataset!

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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 your Machine Learning here! ML in Python is a quite large topic, so be many subjects will only be touched lightly. Nonetheless, I try to give just enough samples and basics to get your first ML models running in there!

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