Amazon, one of the largest purveyors of web services to business and consumers alike, has announced that it will be adding machine learning integration into its web services. Over the years, the company has gotten quite good at suggesting products to you based on your past purchases using their machine learning algorithm. Additionally, Amazon has used machine learning to make Amazon Echo one of the best voice assistants on the market. Now, organizations that utilize Amazon Web Services will have access to a powerful tool to enable them to take their businesses to the next level and increase their bottom line.
Machine learning is used by banks to verify purchases and by retailers to suggest new products on individual sites and through emails
We are on the cusp of a technological revolution poised to change the way we interact with technology and how products are marketed to us. Indeed, we may soon find ourselves in a shopping experience akin to what we saw in Minority Report. Our ability to create new data points via new products (such as the Echo) and services has allowed us to create and continuously improve data models and consumer products. Since 2010, the term Big Data has seeped into the collective vocabulary of more than data scientists and programmers. Innovations like Hadoop have allowed us to process large amounts of data at speeds never before imagined. But then came the question: what on earth do we do with it all? That is where machine learning comes into play.
While machine learning may sound complex, it is actually simpler than you may think. Machine learning algorithms consist of a set of possible models, a method for testing the models, and also a way to find the best model by running the fewest number of tests. A good example of this is trying to find the best restaurant in an area without going and trying food from each of them. In this example, you would use the menu, prices, the geographic location, and other data to create a prediction. Then, that prediction is tested. The true art of this science actually lies in just how much data to use, for too much data can both slow down prediction and introduce too much bias or error. Though that explanation may be a bit simplistic, the 50 billion predictions that Amazon produces weekly help its customers solve problems and make connections that would have otherwise been buried in petabytes of data.
While this may not initially affect us consumers directly, businesses already using Amazon Web Services will now have access to add machine learning immediately. This will allow those who classically would not have had the resources necessary for that level of data analytics to better predict what you want to buy and/or how to keep you interested in their products.
Are you ready for more companies to have grocery store-quality data tracking and analytics? Let us know in the comments.