Microsoft AI (W4 Part 3)

Summary – What are the main points?
The article is generally about machine learning, which is the concept of “getting a computer to act without being explicitly programmed.” (Machine Learning), in the case focusing on Microsofts Azure platform. The argument is based on how Microsofts cloud service is capable of “holding the vast amount of data needed to train machine learning models” and how this would be beneficial to companies wanting to establish their own machine-learning platform. It generally goes over all of the reasons that Microsoft would be a beneficial machine-learning platform to use.
Referenced article:
Heath, N. (2016, December 1) Should Microsoft be your AI and machine learning platform? ZDNet. Retrieved from

Where was the article published?, a business technology news website.

How credible is it?
As with Analytics 3.0, the article is not peer reviewed and is largely written by one person using cited sources and opinions.
The article title creates the impression that the article would be a discussion on why Microsoft would be a beneficial platform to use, but also doesn’t really address the benefits of other cloud-services. It does briefly mention Google cloud services while discussing availability of the services, and that “the cloud-based machine-learning marketplace is increasingly crowded“, but doesn’t really discuss any of the other cloud-services in comparison to Azure to demonstrate how it would be a benefit.

What other articles has the author written? Do they lend credibility?
The author is a Senior Reporter who writes about technology, and from his user page on the ZDNet site you can find a listing of articles he has written. Most of these are news reports, not research papers, and beyond being about IT related subjects they don’t lend much credibility due to news reports being a less reliable source of information in general.

How much other work has been written about the subject? And how does this affect the credibility?
I found a number of articles which wrote about Machine learning itself (Genetic Algorithms, MCMC, Oil Spill Detection, Pattern Recognition, Python, Text Catagorisation), and although many articles in general won’t be discussing platforms for machine learning the amount of articles still affects credibility in that it demonstrates the articles accuracy on machine learning itself – If the article discussed the benefits of Microsoft Azure for machine learning but got the fundamental details of machine learning incorrect, the article would come across as less credible. There being established knowledge in the area shows their argument is based on facts about machine learning.