How to get started with Azure Machine Learning?


This was a question from a university researcher in the Pacific northwest. The good news is that Azure Machine Learning or Azure ML is a powerful native Platform as a Service (PaaS) offering which has been around since 2015.   Mark Garcia, a Cloud Solution Architect at Microsoft, put together a FAQ around Azure Machine Learning:


What is our definition of Machine Learning?

When you think ML many different things fall into this like AI, neural networks, predictive outcomes. Our ML definition is simple:

“Experience” = past data + human input.

Past data is often huge – the quantity of data is doubling about every 18 months and that’s only increasing from here. Computers can consider far more variables than a human making the same decision.

And what do we mean by human input? Human input takes two forms – the input of the user who is either communicating that the output is what they are looking to see or not. In the case that it’s not, the machine can either self-adjust to deliver better results moving forward or the advanced analytic developer or data scientist can make those changes to the model.


How does Azure ML work?

Here is a flow diagram for Azure ML:


Read more on:


Where to go to understand what Azure ML provides

How to get started with Azure ML

Useful hands on labs you can use to see what Azure ML can do

Languages supported by Azure ML

Azure ML APIs and Azure ML Experiments published for you to leverage

Data sources are supported for Azure ML


You can fin these and other topics at Mark`s blog post.


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