Amazon Web Services Expands AI, Machine Learning, IoT Initiatives

Amazon Web Services (AWS) expanded its artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT) initiatives Nov. 29 as company executives revealed a variety of new services and even a wireless, deep learning-enabled video camera at its re:INVENT conference in Las Vegas.

Amazon has a long heritage of using ML and many AWS customers have already started using its ML technology, AWS CEO Andy Jassy said during a Nov. 29 keynote. But ML is “still too complicated for everyday developers” and “there just aren’t that many expert machine learning practitioners in the world” today, he said. Most of the few ML experts there are “end up living at the big technology companies,” he noted.

“If you want to enable most enterprises of companies to be able to use machine learning in an expansive way, we have to solve the problem of making it accessible for everyday developers and scientists,” he said. Building ML models alone is challenging and time-consuming and what inevitably happens is that many developers “throw up their hands in frustration” and give up because it’s “too much work,” he noted.

To solve those problems, AWS has developed the Amazon SageMaker service, he disclosed, calling it “an easy way to build, train and deploy machine learning models for everyday developers.”

“Amazon Sagemaker takes away most of the muck of machine learning, making it easier and faster to build, train, tune and deploy custom models,” Matt Wood, AWS GM-AI, told the conference, before demonstrating how Sagemaker can be used to build a music recommendation service.

Jassy also unveiled AWS DeepLens, which the company called “the world’s first wireless, deep learning enabled video camera for developers.” The HD camera, which works in conjunction with Sagemaker, will be available at early next year at $249, Amazon said. “We believe that you’ll be able to get started running your first deep learning computer vision model in 10 minutes from the time that you unbox the camera,” according to Jassy.

The National Football League (NFL), meanwhile, selected AWS’s ML and data analytics services to enhance the accuracy, speed and insights provided by the Next Gen Stats platform, the NFL’s player-tracking system.

The NFL is looking to develop new ways to visualize action on the football field by uncovering deeper insights into plays and expand fans’ experiences by offering a broader range of advanced statistics, AWS and the NFL said. AWS also became an “official technology partner” of the NFL for Next Gen Stats, Michelle McKenna-Doyle, NFL SVP and CIO, told the conference. AWS’s services are easy to use and will make it more efficient and faster to collect data during games, she said.

AWS also announced several new services and capabilities for connected, Internet of Things (IoT) devices at the edge that it said in a news release will “make getting started with IoT as easy as one click, enable customers to rapidly onboard and easily manage large fleets of devices, audit and enforce consistent security policies, and analyze IoT device data at scale.”

Among those new initiatives is Amazon FreeRTOS, an operating system that extends the functionality of AWS IoT to devices with very low computing power, such as lightbulbs, smoke detectors and conveyor belts, the company said. AWS Greengrass ML Inference, meanwhile, is a new capability for AWS Greengrass that allows ML models to be “deployed directly to devices, where they can run machine learning inference to make decisions quickly, even when devices are not connected to the cloud,” it said. AWS Greengrass is software that lets users securely run local compute, messaging, data caching and sync capabilities for connected devices.