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Amazon Fraud Detector now generally available


Amazon Web Services (AWS) has announced the general availability of its machine learning-based fraud detection service. Amazon Fraud Detector is a fully managed service touted as making it easy to quickly identify potentially fraudulent online activities, such as online payment and identity fraud, the creation of fake accounts, and loyalty account and promotion code abuse "in milliseconds". To use the service, customers can select a pre-built machine learning model template; upload historical event data of both fraudulent and legitimate transactions to build, train, and deploy machine learning models; and create decision logic to assign outcomes to the predictions. "Customers of all sizes and across all industries have told us they spend a lot of time and effort trying to decrease the amount of fraud occurring on their websites and applications," Amazon machine learning VP Swami Sivasubramanian said. "By leveraging 20 years of experience detecting fraud coupled with powerful machine learning technology, we're excited to bring customers Amazon Fraud Detector so they can automatically detect potential fraud, save time and money, and improve customer experiences -- with no machine learning experience required."

AWS CEO Andy Jassy On Channel Conflict, Competition And AI


"There's this folklore mythology around if Amazon launches a business in a certain area, it means that all the other businesses in those areas are not going to be as successful," Jassy said at the Goldman Sachs Technology and Internet Conference in San Francisco yesterday. "I just haven't seen it." There are only two significant industries that Amazon has "disrupted," according to Jassy: retail with, and technology infrastructure with AWS. His remarks come as federal and state regulators are conducting antitrust probes to determine whether Amazon and other technology giants stifle competition and innovation. "In both cases, they were models that were pretty antiquated, and customers weren't so happy with those models, and somebody was going to end up reinventing them," Jassy said.

AWS details steps to digital transformation, highlights machine learning


LAS VEGAS – Amazon Web Services (AWS) chief executive officer Andy Jassy highlighted 27 new services during his keynote at the company's eighth annual re:Invent learning and education conference and urged customers not to procrastinate in starting their digital transformations. He told the 65,000 attendees that not all of the key components in the process are technology elements. First, he said, the senior leadership team needs to be aligned behind the effort. Without that, it's easy for dissenters to block the initiatives. Second, set top-down aggressive goals.

AWS's Web-based IDE for ML Development: SageMaker Studio


AWS, Azure, Google Cloud, IBM Cloud, Oracle – they'll all vying to become the dominant force of gravity in the public cloud services market, and among the most fiercely fought over areas of cloud leadership is AI/machine learning enablement. Given that AI's TAM is roughly * and that the FAANGs are out ahead of everyone on AI expertise, it makes sense they would commercialize the technologies they use and that they've developed to attract enterprise AI customers to their platforms. A centerpiece of AWS's AI market strategy is SageMaker, a managed service that provides developers and data scientists who aren't necessarily ML experts with the tools to build, train and deploy ML models. Launched two years ago, AWS has designed SageMaker to lighten the heavy lifting from each step of the machine learning process. Since its inception, the product suite has been expanded into SageMaker Studio, which AWS CEO Andy Jassy, at the annual re:Invent conference in Las Vegas this week, described as an integrated, web-based IDE (interactive development environment) for machine learning that lets developers collect and store code, notebooks, data sets, settings and project folders in a single setting.

NFL-AWS partnership hopes to reduce head injuries with machine learning – TechCrunch


Today at AWS re:Invent in Las Vegas, NFL commissioner Roger Goodell joined AWS CEO Andy Jassy on stage to announce a new partnership to use machine learning to help reduce head injuries in professional football. "We're excited to announce a new strategic partnership together, which is going to combine cloud computing, machine learning and data science to work on transforming player health and safety," Jassy said today. NFL football is a fast and violent sport involving large men. Injuries are a part of the game, but the NFL is hoping to reduce head injuries in particular, a huge problem for the sport. A 2017 study found that 110 out of 111 deceased NFL players had chronic traumatic encephalopathy (CTE).

AWS launches major SageMaker upgrades for machine learning model training and testing


Amazon today announced half a dozen new features and tools for AWS SageMaker, a toolkit for training and deploying machine learning models to help developers better manage projects, experiments, and model accuracy. AWS SageMaker Studio is a model training and workflow management tool that collects all the code, notebooks, and project folders for machine learning into one place, while SageMaker Notebooks lets you quickly spin up a Jupyter notebook for machine learning projects. CPU usage with SageMaker Notebooks can be managed by AWS and quickly transfer content from notebooks. There's also SageMaker Autopilot, which automates the creation of machine learning models and automatically chooses algorithms and tunes models. "With AutoML, here's what happens: You send us your CSV file with the data that you want a model for where you can just point to the S3 location and Autopilot does all the transformation of the model to put in a format so we can do machine learning; it selects the right algorithm, and then it trains 50 unique models with a little bit different configurations of the various variables because you don't know which ones are going to lead to the highest accuracy," CEO Andy Jassy said onstage today at re:Invent in Las Vegas.

AWS Launches New EC2 Arm-Based, Machine-Learning Inference Instances


Amazon Web Services unveiled new EC2 Arm-based instances powered by its AWS-designed Graviton2 processors, along with Inf1 machine-learning inference instances powered by its custom AWS Inferentia chips. "If you look at instances to start, it's not just that we have meaningfully more instances than anybody else, but it's also that we've got a lot more powerful capabilities in each of those instances," AWS CEO Andy Jassy said in his keynote address at the AWS re:Invent 2019 conference in Las Vegas. AWS' pace of innovation has resulted in a significant increase in instances, Jassy said, and AWS now has four times more types today than two years ago. "We have the most powerful GPU machine-learning training instances, most powerful GPU graphics rendering instances, the largest in-memory instances for SAP workflows with 24 terabytes, the fastest processors in the cloud with the z1d," Jassy said. "You've got the only standard instances that have 100 Gigabits per second of network connectivity, the only instances that have all the processor choices from Intel and AMD. A very different set of capabilities on the instances side."

AWS aims to bring machine learning, natural language processing to call center ZDNet


Amazon Web Services (AWS) has packaged up another in-house Amazon capability and made it available to customers, having announced Contact Lens for Amazon Connect on Tuesday. CEO Andy Jassy has touted that Contact Lens for Amazon Connect -- the company's omnichannel cloud contact centre service -- will stitch together new abstractions for machine learning so AWS customers can have an easy to consume function. "Amazon Connect is one of the fastest growing services in the history of AWS … off to a blazing start," he said during the day one keynote of AWS re:Invent in Las Vegas. "Using the same customer service technology Amazon has used … it's really easy to use." According to Jassy, Connect is the first call centre in the cloud with machine learning in mind.

AWS, Verizon team up to deliver 5G edge cloud computing - Express Computer


Amazon's Cloud arm Amazon Web Services (AWS) and US carrier Verizon Communications have announced a partnership that will bring the power of the world's leading cloud closer to mobile and connected devices at the edge of Verizon's 5G Ultra-Wideband network. Verizon is the first technology company in the world to offer 5G network edge computing, and will use new service, AWS Wavelength, to provide developers the ability to deploy applications that require ultra-low latency to mobile devices using 5G, the companies said in a statement during AWS re: Invent conference. The companies are currently piloting AWS Wavelength on Verizon's edge compute platform, 5G Edge, in Chicago for a select group of customers, including video game publisher Bethesda Softworks and the National Football League (NFL). Additional deployments are planned in other locations across the US in 2020. "We are first in the world to launch Mobile Edge Compute -- deeply integrating Verizon's 5G Edge platform with Wavelength to allow developers to build new categories of applications and network cloud experiences built in ways we can"t even imagine yet," said Hans Vestberg, CEO and Chairman of Verizon. "We've worked closely with Verizon to deliver a way for AWS customers to easily take advantage of ubiquitous connectivity and advanced features of 5G," added AWS CEO Andy Jassy. "While some ultra-low latency use cases like smart cars, streaming games, VR, and autonomous industrial equipment are well understood today, we can"t wait to see how builders use 5G edge computing to delight their mobile end users and connected device customers," Jassy added.

SageMaker Studio makes model building, monitoring easier


AWS launched a host of new tools and capabilities for Amazon SageMaker, AWS' cloud platform for creating and deploying machine learning models; drawing the most notice was Amazon SageMaker Studio, a web-based integrated development platform (IDE) . In addition to SageMaker Studio, the IDE for platform for building, using, and monitoring machine learning models, the other new AWS products aim to make it easier for non-expert developers to create models and to make them more explainable. During a keynote presentation at the AWS re:Invent 2019 conference here Tuesday, AWS CEO Andy Jassy described five other new SageMaker tools: Experiments, Model Monitor, Autopilot, Notebooks, and Debugger. "SageMaker Studio along with SageMaker Experiments, SageMaker Model Monitor, SageMaker Autopilot, and Sagemaker Debugger collectively add lots more lifecycle capabilities for the full ML (machine learning) lifecycle and to support teams," said Mike Gualtieri, an analyst at Forrester. SageMaker Studio, Jassy claimed, is a "fully-integrated development environment for machine learning."