Sunday, June 16, 2019

Visual interface for Azure Machine Learning Service

Microsoft has been great with interfaces for visualizing workflows. SharePoint has it, Microsoft Flow has it and many other services including Logic Apps has it. The preview for the Azure ML visual interface was announced during Microsoft Build. 

Not only visualizing and understanding, drag and drop helps ease the processes of testing and deploying ML models as well. Nothing helps more than a visual diagram for a developer to understand the logic of, may be his own work sometime later. 

Source: Azure Blog

Who may use it?
  • Newbies

I would rank people who are new to machine learning as the best advantaged group of people. Visual diagrams will help people who have very less experience as the toolbox of items (experiment items) are clearly visible on the left. So, if you are in need of finding a reason to get on board, here we got one.
  • Scientists with arts in blood

There are data scientists who believe in the common say 'a picture says a thousand words'. It makes things very easy when you evaluate a model after sometime or if things are not well documented elsewhere. 
  • ML Experts

Well, I am not an ML expert to say how this helps an expert all the way. But simply, ease of not needing to install any software itself may be a reason for an expert to use it.  

Scalability and Ease of Deployment

We do not ride a Lambogini for the drive test. As same, we start small on building models. But, with a fear of whether we can actually scale up the model for production or much higher level training. Visual interface for Azure Machine Learning service, this is much easier.

Best thing about Azure and development is that it takes only seconds to deploy to production for many services. Same applies for machine learning. The next second you spend on after testing, you can deploy the model to production. 

Enabling Visual Interface preview is very easy. Just go to the ML workspace on Azure Portal and then select Visual Interface and launch. 
The full article with more details at Microsoft Azure blog referenced can be found here

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