Leveraging full AWS integration

The AWS data ecosystem of pipelines, storage, environments, and Artificial Intelligence (AI) is also a strong argument in favor of choosing Amazon ML as a business platform for its predictive analytics needs. Although Amazon ML is simple, the service evolves to greater complexity and more powerful features once it is integrated in a larger structure of AWS data related services. 

AWS is already a major actor in cloud computing. Here's what an excerpt from The Economist, August  2016 has to say about AWS (http://www.economist.com/news/business/21705849-how-open-source-software-and-cloud-computing-have-set-up-it-industry):

AWS shows no sign of slowing its progress towards full dominance of cloud computing's wide skies. It has ten times as much computing capacity as the next 14 cloud providers combined, according to Gartner, a consulting firm. AWS's sales in the past quarter were about three times the size of its closest competitor, Microsoft's Azure.

This gives an edge to Amazon ML, as many companies that are using cloud services are likely to be already using AWS. Adding simple and efficient machine learning tools to the product offering mix anticipates the rise of predictive analytics features as a standard component of web services. Seamless integration with other AWS services is a strong argument in favor of using Amazon ML despite its apparent simplicity.

The following architecture is a case study taken from an AWS January 2016 white paper titled Big Data Analytics Options on AWS (http://d0.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf), showing a potential AWS architecture for sentiment analysis on social media. It shows how Amazon ML can be part of a more complex architecture of AWS services: