Machine Learning Gets Its Day At Amazon Conference

Machine learning was one of the main subjects on Tuesday (July 17) during the Amazon AWS Summit in New York City, as the eCommerce operator rolled out new cloud-based features related to commerce and payments.

The announcements from Amazon Web Services (AWS) come at a time when machine learning and AI are receiving increased focus in the payments and commerce industries, with executives and researchers developing technologies that can operate outside the limitations of human bias and intuition.

In fact, an upcoming PYMNTS webinar with Karen Webster and Sunil Madhu, founder of identity verification and fraud prevention services provider Socure, will include discussion about how a robust AI robot could leverage the full power of machine learning paired with massive data sets, drawing in data from online, offline and social sources.

In New York on Tuesday, AWS said it had built machine learning and AI improvements into its cloud computing services designed for enterprises. Two of the improvements are meant to speed up the AWS SageMaker service, which customers can use to test and deploy custom AI algorithms.

According to Matt Wood, general manager for machine learning for AWS, the new SageMaker streaming algorithms enable users to stream large amounts of training data, which developers employ when constructing custom AI models.

The new product results in an “up to 90 percent reduction in the amount of time it takes for training to start,” he told summit attendees. Reduced training time can lead to “reduced training costs” for the developers who use AWS.

Wood also announced the launch of SageMaker Batch Transform. It employs a “simple API” to enable user to process “data dumps in a batch,” he said, including information related to payments. Such a capability can also provide time savings.

Machine learning is a subset of AI where computer algorithms are created to crawl through large quantities of data and identify patterns, which it then uses to solve problems. Machine learning is used in a variety of sectors — a fact underscored by recent news that Bessemer Venture Partners is launching a $10 million early-stage seed program to back new startups using the technology, with the goal of bringing new products and processes to the healthcare industry.

“Machine learning is in virtually every industry, and every company,” Wood said, adding that over the past year, there has been a 250 percent increase in developers doing machine learning via Amazon Web Services. Late last year, in fact, Amazon launched a bevy of new machine learning features designed for AWS customers to develop and train custom AI algorithms.

Also at the AWS Summit on Tuesday (July 17), Amazon announced that its Amazon Transcribe technology now supports channel synthesis, “which can take multi-channel audio streams and construct a single transcription from them.”

As well, Wood said that the new AWS syntax identification tool can help users dig deeper into customer reviews. The technology analyzes the text in those reviews in such a way that AWS customers can learn more about not only what consumers think of specific models, but also the color and other attributes within those models. “You get a sense of customer sentiment about all those individual products,” he noted.



The How We Shop Report, a PYMNTS collaboration with PayPal, aims to understand how consumers of all ages and incomes are shifting to shopping and paying online in the midst of the COVID-19 pandemic. Our research builds on a series of studies conducted since March, surveying more than 16,000 consumers on how their shopping habits and payments preferences are changing as the crisis continues. This report focuses on our latest survey of 2,163 respondents and examines how their increased appetite for online commerce and digital touchless methods, such as QR codes, contactless cards and digital wallets, is poised to shape the post-pandemic economy.