Why might companies use Amazon Machine Learning and related tools?

One of the most fundamental reasons – probably the most fundamental reason – to use the Amazon Machine Learning (AML) cloud-based platform is to allow a company’s employees or contractors to implement machine learning programs without a high level of technical skill. AML is a support system for “non-techies” who want to utilize the power that machine learning has to innovate in business.

Amazon offers the Amazon Machine Learning platform as an environment that allows for guided machine learning implementation, with implementation wizards as well as dashboard and visualization tools that make using ML algorithms easy and straightforward.

With that said, companies use these machine learning algorithms and programs toward a variety of goals and purposes. One is the creation of “smart applications” that can accomplish sophisticated results based on machine learning. Building and integrating machine learning into applications allows them to evolve past the restrictions of their original programming, and develop more functionality based on those high-powered algorithms that users are installing with the assistance of the Amazon platform.

Companies can also utilize the power of Amazon Machine Learning for various types of data-driven development – for instance, customer tracking, finding problem spots in interface, developing better product outreach or improving a customer experience. Different kinds of user analytics serve a business well in terms of strategic planning.

Another major use of machine learning supported by the AML platform is the development of systems that reinforce sales at a particular point of failure. This is something that's often talked about in the context of the artificial intelligence which machine learning algorithms foster and help to develop.

One excellent example is shopping cart abandonment. Companies may employ their workers to use Amazon Machine Learning to set up virtual “shopping cart abandonment helpers” that do certain tasks when a customer leaves a shopping cart rather than converting and making a purchase. For example, the machine learning algorithms can identify when to activate a quick script that will send a follow-up message questioning that user about his or her intentions, or requesting that they complete their purchase, in a polite and friendly way.

To accomplish all of these different goals, companies have to build intuitive models and automate machine learning with particular APIs and SDKs. All of this is well served with the Amazon Machine Learning platform that basically acts as a tutorial or guide for those who don't have extensive experience with the underlying nuts and bolts of the algorithms themselves. In much the same way that Dreamweaver and other early editor tools offered users an easier way to use HTML for web design, Amazon Machine Learning offers users an easier way to master one of the biggest and most important elements of artificial intelligence in the technology market right now.