Introduction: Azure Machine Learning Studio is an amazing tool that lets us create efficient ML experiments with simple drag and drop features. We can predict anything from Flight Predictions to Churn Analysis. But what if we want to represent this predicted data a more visually appealing format? Well it is possible to do this by representing your predictions on Power BI! Pre-Requisites: Basic Understanding of Azure Machine Learning Studio. Basic Understanding of Power BI. A Blob Container created on Azure Storage.   Steps: Create your Azure Machine Learning Experiment on Azure Machine Learning Studio. Convert your Training Experiment to a Predictive Experiment and Deploy it as a Web Service. We will create a Console application in Visual Studio and copy paste the code inside Batch Execution. For automation we can create automated data pipelines but for now we will just use a simple Console application. Remove the existing code from the Console Application and copy paste the Batch Execution code. Install the necessary Nuget Packages and also update the following parameters. – BaseURL will be the same. – Storage Account Name, Storage Account Key and Storage Container Name will be parameters that can be found in your Azure Blob Storage which was created. – Api Key can be found in the Web Experiment Page in Azure Machine Learning Studio. – The input path is the path where you have saved your input csvfile for Batch Execution. Your Input csv file should have all the features which you have used to train your experiment After you run your Console application a new output1results.csv file should get generated in your Blob Container. The output results should include the labels which your experiment generates in it’s output. It should include the Scored Labels and Scored Probabilities labels as well. Now you can get your data using Azure Blob Storage as your source in Power BI and use the columns in the output1result.csv file to generate your ML Predicted Reports. The Report can look something like this. I hope this blog helps you to combine Azure Machine Learning Studio and Power BI to create a powerful predictive solution.

Introduction: Narratives for Power BI is a product that automatically delivers dynamic narratives that explain the insights within your data. No more manually writing explanations and spending time interpreting data. Instead, the narratives, which are powered by advanced analytics, are perceptive and dynamic and explain what is most interesting and important in your data. Drill down deeper into your data and watch narratives update in real-time during the data discovery process Steps: Go to powerbi.narrativescience.com and enter your business email id.  A link for downloading the extension and installation instructions will be mailed to you A pibiviz file will be downloaded on downloading the extension. A pbiviz file is nothing but a custom visual which can be imported in Power BI Desktop. Import the file on Power BI Desktop Benefits: Automated Narratives generated that give more detailed insights about the report which may not even be obvious Real time update on interaction with data Many customization options to personalize your narrative Click on Narrative and select Dimensions and Values based on which Narratives will be generated. After selecting the fields you will have to select your narrative type. Discrete: For distinct data like that in Bar Charts Continuous: For continuous data like that in Line Charts Percent of Whole: For data by percentage like Pie Charts Scatterplot: For data based on scatterplot like Charts A narrative gets generated It also changes on real time interaction The type structure and verbosity can be customized in the Format Pane Type Can be Discrete, Continuous, Percent of Whole or Scatterplot. Structure can be either in Paragraph format or Bullet Points. Verbosity the level of information displayed. Low verbosity would show less detailed narrative with high level information while High verbosity would show a very detailed narrative. Medium verbosity would be a midway between both. I hope this blog encourages you to use this powerful extension to improve your reports by making it as detailed as possible with minimalistic efforts!