In today’s blog we will discover some useful insights about using a created model on Azure Machine Learning Studio and call that model in our power bi so we have data in power bi and use that API as a tool for machine learning.

Before we move any further let me remind you that there are various options to consume machine learning in power bi but for this blog post we will only be covering couple of them.

So, first we will be covering Microsoft Standard to invoke an Azure Machine Learning Studio (classic) model into Power BI. Here the following steps you can follow to invoke a machine learning model into power bi:
1- create an Azure ML model if you don’t already have a model and publish it.
2- Next we have to access  Azure Machine Learning model from power bi, To do that we have to get a Reader role from Azure subscription.
3- Now we have to create a Dataflow in power bi whom you granted access to Azure Machine Learning Model. After you signed in:
– create a workspace and navigate to a workspace on your dedicated capacity that has
the AI preview enabled and Select Add new entities.
– Upload the dataset Text/CSV File as our data source
4- In the last step we will apply insights from Azure Machine Learning model, navigate to AI Insights button in the ribbon, and from Azure Machine Learning Models folder navigate to the Azure ML models to which you’ve been granted access are listed as Power Query functions with a prefix AzureML.

To invoke an Azure Machine Learning model, we will specify our input parameters such as (timestamp, air pressure, angular speed, piston speed and piston vibration). In last step, select Invoke to view the preview of the Azure ML model’s output as a new column in the entity table.

Now, we can also consume a machine learning webservice in power bi which is much easier to integrate for free. To achieve the task, we have to follow these steps:
1- You should have an Azure Machine Learning model and deployed as a webservice.
2- Import the dataset from your local computer and navigate to the query editor
3- next we need to navigate to the Run R script where we connect our Azure Machine Learning model with Power BI. For the following step you have to have the following credentials from Azure Machine Learning Studio workspace: (1) workspace Id (2) Authentication token (3) Service name. Next hit the OK button to view the result in output column.

I hope you found this blog post helpful. If you have any questions, please feel free to contact info@kaispe.com.

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Today we will be evaluating the difference between of Azure Machine Learning Services and Azure Machine Learning Studio that will help us to choose the best option to develop our Machine Learning solution.

First lets start with Azure Machine Learning Studio which offers a robust set of tools designed to develop, deploy and manage machine learning projects.

Azure Machine Learning Studio

• Azure Machine Learning Studio is a collaborative, drag-and-drop environment where you have No Coding is required and It’s a tool where you can use to build, test, and deploy predictive analytics solutions on your data.
• Uses pre-build and pre-configured machine learning algorithm and data handling modules as well as proprietary compute platform.
• Azure Machine Learning Studio does not have much control on training scalability.
• It has standard experiments but you can get quick results in lesser time.

Second, we have is Azure Machine Learning Services which you can find in two flavors, a Python SDK and a drag-and-drop style Visual Interface(preview).

Azure Machine Learning Service

• Azure Machine Learning Service provides both SDKs (Code based) and visual interface to quickly prepare data, train and deploy machine learning models.
• We can have the same drag-and drop experience to Machine Learning Studio, however, unlike proprietary compute platform of studio, the Visual Interface uses your own compute resources.
• It integrates with python environment, frameworks and tools and it does not restrict you what you need to use.
• Azure Machine Learning Service have full control on training scalability with custom compute targets.

Now, as we have got the idea about both Azure Machine Learning Studio and Azure Machine Learning Service, we can assume that from being a developer Azure Machine Learning Service is more flexible compare to Azure Machine Learning Studio.

I hope this short post has helped you get a better idea about Microsoft Azure Machine Learning offering products. If you have any questions, please feel free to contact me muhammad.ahmad@kaispe.com

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