Microsoft Azure Review

The Canvas flow interface brings a very nice functionality that improves self-learning.


What is most valuable?

The Canvas flow interface brings a very nice functionality that improves self-learning.

How has it helped my organization?

Selecting and applying a machine learning algorithm is not a click-and-run process. In this case, clustering our data has helped us to find patterns and trends that were not visible using conventional (internal) classification.

What needs improvement?

Stronger R integration in a circular fashion (Azure->R->Azure). Nowadays, it has an unidirectional bias.

For how long have I used the solution?

I've used Machine Learning Studio for the last six months.

What was my experience with deployment of the solution?

We've had no issues with the deployment.

What do I think about the stability of the solution?

By using a free account for cloud services can bring you "queued" status, but normally it runs smoothly. The quota defined for free account is enough for a variety of experiments and none of the features are blocked.

What do I think about the scalability of the solution?

We've had no issues with the scalability.

How are customer service and technical support?

Customer Service:

As a cloud service, online chat and email service are available, but the community forums are the best place to solve issues.

Technical Support:

We have a support maintenance agreement for Windows/Office.

Which solution did I use previously and why did I switch?

There are a few competitors in place, and their number is increasing, but friendliness is the strong point of AzureML, comparing to Amazon AWS or BigML.

How was the initial setup?

It does not involve any setup, just having an Outlook or Microsoft account.

What's my experience with pricing, setup cost, and licensing?

Using a free account leads to offline projects. For online projects those issues must be evaluated according to client side company's environment.

What other advice do I have?

Machine learning is just one part of the whole data science cycle. Big data (streaming, video, etc.) or deep learning needs must be addressed with additional tools. But for prediction/classification this is a fantastic tool.

Below is a PCA graph generated by K-means training model and its Qlik Sense panel. The idea is to segment unlabeled data based on numerical features in order to find common patterns that can be grouped, named clusters.

**Disclosure: I am a real user, and this review is based on my own experience and opinions.
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