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Amazon Web Services Announces 13 New Machine Learning Services and Capabilities, Including a Custom Chip for Machine Learning Inference, and a 1/18 Scale Autonomous Race Car for Developers

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AWS Deepracer, a 1/18th Scale Autonomous Racing Car, Which Gets Developers Rolling with Reinforcement Learning

AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company , announced 13 new machine learning capabilities and services, across all layers in the machine learning stack, to help put machine learning in the hands of even more developers. AWS introduced new Amazon SageMaker features making it easier for developers to build, train, and deploy machine learning models – including low cost, automatic data labeling and reinforcement learning (RL). AWS revealed new services, framework enhancements, and a custom chip to speed up machine learning training and inference, while reducing cost. AWS announced new artificial intelligence (AI) services that can extract text from virtually any document, read medical information, and provide customized personalization, recommendations, and forecasts using the same technology used by Amazon.com. And, last but certainly not least, AWS will help developers get rolling with machine learning with AWS DeepRacer, a new 1/18th scale autonomous model race car for developers, driven by reinforcement learning.

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“Amazon Elastic Inference opens new doors that enables us to explore running workflows more cost effectively at scale.”

These announcements continue the drum beat of machine learning innovation from AWS, which has launched more than 200 significant machine learning capabilities in the past 12 months. Customers using these new services and capabilities include Adobe, BMW, Cathay Pacific, Dow Jones, Expedia, Formula 1, GE Healthcare, HERE, Intuit, Johnson & Johnson, Kia Motors, Lionbridge, Major League Baseball, NASA JPL, Politico.eu, Ryanair, Shell, Tinder, United Nations, Vonage, the World Bank, and Zillow.

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“We want to help all of our customers embrace machine learning, no matter their size, budget, experience, or skill level,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning. “Today’s announcements remove significant barriers to the successful adoption of machine learning, by reducing the cost of machine learning training and inference, introducing new SageMaker capabilities that make it easier for developers to build, train, and deploy machine learning models in the cloud and at the edge, and delivering new AI services based on our years of experience at Amazon.”

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