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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

November 28, 2018 GMT

SEATTLE--(BUSINESS WIRE)--Nov 28, 2018--Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), 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 1/18 th scale autonomous model race car for developers, driven by reinforcement learning.


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. To learn more about AWS’s new machine learning services, visit: https://aws.amazon.com/machine-learning.

“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.”


New infrastructure, a custom machine learning chip, and framework improvements for faster training and low-cost inference

Most machine learning models are trained by an algorithm that finds patterns in large amounts of data. The model can then make predictions on new data in a process called ‘inference’. Developers use machine learning frameworks to define these algorithms, train models, and infer predictions. Frameworks (such as TensorFlow, Apache MXNet, and PyTorch) allow developers to design and train sophisticated models, often using multiple GPUs to reduce training times. Most developers use more than one of these frameworks in their day-to-day work. Today, AWS announced significant improvements for developers building with all of these popular frameworks, by improving performance and reducing cost for both training and inference.

Autodesk is a leader in 3D design, engineering, and entertainment software who uses deep learning models for use cases ranging from exploring thousands of potential design alternatives, semantically searching designs and optimizing the engineering construction process to applying better UV mapping in photo realistic renderings. “Running efficient inference is one of the biggest challenges in machine learning today,” said Peter Jones, Head of AI Engineering for Autodesk Research. “Amazon Elastic Inference is the first capability of its kind we’ve found to help us eliminate excess costs that we incur today from idle GPU capacity. We estimate it will save us 75 percent in costs compared to running GPUs.”

EagleView, a property data analytics company, helps lower property-damage losses from natural disasters by decreasing the time it takes to assess damage so that homeowners can decide next steps much faster. Using aerial, drone, and satellite images, EagleView runs deep learning models on AWS to make quicker, more accurate assessments of property damage within 24 hours of a natural disaster. “Matching the accuracy of human adjusters in property assessments requires us to process massive amounts of data in the form of ultra-high resolution images covering the entire multi-dimensional space (spatial, spectral, and temporal) of a disaster-affected region,” explains Shay Strong, Director of Data Science and Machine Learning at EagleView. “Amazon Elastic Inference opens new doors that enables us to explore running workflows more cost effectively at scale.”

New Amazon SageMaker capabilities make it easier to build, train, and deploy machine learning; developers get hands on with AWS DeepRacer, a 1/18 th scale autonomous race car driven by reinforcement learning

Amazon SageMaker is a fully managed service that removes the heavy lifting and guesswork from each step of the machine learning process. Amazon SageMaker makes it easier for developers to build, train, tune, and deploy machine learning models. Today, AWS announced a number of new capabilities for Amazon SageMaker.

Tyson Foods is one of the world’s largest food companies and a recognized leader in protein. “We are building a computer vision system for our chicken processing facilities and we need highly accurate labeled training datasets to train these systems,” said Chad Wahlquist, Director of Emerging Technology for Tyson Foods. “When we first tried to setup our own labeling solution, it required a large amount of compute and a Frankenstein of open source solutions – even before creating the user interface for data labeling. With Amazon SageMaker Ground Truth, we were able to use the readymade template for bounding boxes and got a labeling job running in just a few clicks, quickly and easily. Amazon SageMaker Ground Truth also enables us to securely bring our own workers to label the data, which is an essential requirement for our business. We are looking forward to using Amazon SageMaker Ground Truth across our business.”

Dubbed “America’s Un-carrier,” T-Mobile is a leading wireless services, products, and service innovation provider. “The AI at T-Mobile team is integrating AI and machine learning into the systems at our customer care centers, enabling our team of experts to serve customers with greater speed and accuracy through Natural Language Understanding models that show them relevant, contextual customer information in real-time,” said Matthew Davis, Vice President of IT Development for T-Mobile. “Labeling data has been foundational to creating high performing models, but is also a monotonous task for our data scientists and software engineers. Amazon SageMaker Ground Truth makes the data labeling process easy, efficient, and accessible, freeing up time for them to focus on what they love – building products that deliver the best experiences for our customers and care representatives.”

Chick-fil-A, Inc. is a family owned and privately held restaurant company that is known for its original chicken sandwich and which serves freshly prepared food in more than 2,300 restaurants in 47 states and Washington, DC. “Food safety is of critical importance in our business. Our early efforts with computer vision and machine learning show promise in improving operations,” said Jay Duff, Principal Team Lead for Chick-fil-A. “Amazon SageMaker and GroundTruth helped us speed up the development of new models and evaluations by making it easier to label and verify new training sets, re-train models, and then iterate on more complex data. Additionally, the workforce management features gave us faster turnaround on manual tasks while reducing administrative toil.”

Arm technology is at the heart of a computing and connectivity revolution that is transforming the way people live and businesses operate. “Arm’s vision of a trillion connected devices by 2035 is driven by the additional consumer value derived from innovations like machine learning,” said Jem Davies, fellow, General Manager and Vice President for the Machine Learning Group at Arm. “The combination of Amazon SageMaker Neo and the Arm NN SDK will help developers optimize machine learning models to run efficiently on a wide variety of connected edge devices.”

Cadence enables electronic systems and semiconductor companies to create the innovative end products that are transforming the way people live, work, and play. Cadence software, hardware and semiconductor IP are used by customers to deliver products to market faster. “Cadence(r) Tensilica(r) processors are optimized for on-device machine learning applications spanning from autonomous driving cars to speech processing to robotics,” said Babu Mandava, Senior Vice President and General Manager of the IP Group at Cadence Design Systems. “Amazon SageMaker Neo simplifies the deployment of optimized models from cloud to the edge. We are excited to be driving a seamless integration of Amazon SageMaker Neo with our Tensilica processor family and development environment to help developers optimize machine learning models for Tensilica-powered edge devices.”

GE Healthcare is a leading provider of medical imaging, monitoring, biomanufacturing, and cell and gene therapy technologies that enables precision health in diagnostics, therapeutics and monitoring through intelligent devices, data analytics, applications and services. “GE Healthcare is transforming healthcare by empowering providers to deliver better outcomes,” said Keith Bigelow, Senior Vice President of Edison Portfolio Strategy, GE Healthcare. “We train computer vision models with Amazon SageMaker that are then deployed in our MRI and X-Ray devices. By applying reinforcement learning techniques, we are able to reduce the size of our trained models while achieving the right balance between network compression and model accuracy. Amazon SageMaker RL enabled us to get from idea to implementation in less than four weeks by removing the complexities of running reinforcement learning workloads.”

“Reinforcement Learning is enabling innovation in machine learning and robotics,” said Brad Porter, Vice President and Distinguished Engineer of Amazon Robotics. “We’re excited Amazon SageMaker is making it easier to try reinforcement learning techniques with real-world applications, and we’re already experimenting with ways to use it for robotic applications. For instance, earlier this year we showed a robot that was able to play beer pong using some of these techniques and we’re excited to continue to explore these opportunities in partnership collaboration with AWS.”

New AI services bring intelligence to all apps, no machine learning experience required

Many developers want to be able to add intelligent features to their applications without requiring any machine learning experience. Building on existing computer vision, speech, language, and chatbot services, AWS announced a significant expansion of AI services.

Cox Automotive is a subsidiary of Cox enterprises which encompasses all of Cox’s global automotive businesses including Kelley Blue Book, Xtime, Autotrader.com, and Manheim. “At Cox Automotive, we are looking to transform how the world buys, sells, and trade cars. To further modernize our automotive solutions, we will be leveraging Amazon Textract to accelerate how quickly cars can be transacted,” said Bryan Landerman, Chief Technology Officer at Cox Automotive. “With Amazon Textract, we can automatically capture and validate data from documents and forms, such as loan applications or vehicle titles, so decisions can be made more quickly. This will reduce customer effort and further streamline the process for everyone involved from the manufacturer to the buyer.”

Alfresco is a leading enterprise open source provider of process automation, content management, and information governance software. “At Alfresco, we want to make document processing and content management as simple as possible for our customers. Since a document management system is only as good as its input, it is critical that we have the foundational tools that can automatically and accurately extract key information from digitized documents,” said John Newton, CTO and Founder at Alfresco. “Previously, we built custom solutions on top of OCR technology in order to extract data of interest, which required intensive manual training. This process consumed valuable time and resources, but it was work that had to be done. With Amazon Textract, we can now automatically extract not just the text in a document and table information, but real insights that allow us to automate data entry and facilitate faster business decisions. Amazon Textract is enabling us to provide greater data integrity, security compliance, and the ability to launch business processes faster than ever. And most importantly, all of this helps us better assist our customers in their digital transformation journey.”

Beth Israel Deaconess Medical Center (BIDMC) is a patient care, teaching, and research affiliate of Harvard Medical School and consistently ranks as a national leader among independent hospitals in National Institutes of Health funding. BIDMC is the official hospital of the Boston Red Sox. “At BIDMC, we have over 490 surgical beds that are always occupied. We strive to quickly and successfully perform surgical procedures so our patients can be treated in time. But a lot of procedures were being canceled and delayed because the patients’ completed History and Physical (H&P) form required before surgery was difficult to locate in the Electronic Health Records (EHR),” said Venkat Jegadeesan, Senior Enterprise Architect of Beth Israel Deaconess Medical Center. “To solve this, we started to use Amazon Comprehend Medical to make data in our EHR systems to be easily searchable using key medical text. Our teams are now able to identify the H&P’s quickly with the right prompts for clinical staff. As a result, we can save a lot of valuable time and help prevent delays or potential cancellations which can be inconvenient for patients and their families.”

At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. “Curing cancer is, inherently, an issue of time,” said Matthew Trunnell, Chief Information Officer, Fred Hutchinson Cancer Research Center. “For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured medical record data. Amazon Comprehend Medical will reduce this time burden from hours to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”

PricewaterhouseCoopers (PwC) is a network of firms in 158 countries with over 250,000 people who are committed to delivering quality in assurance, advisory, and tax services. “Amazon Comprehend Medical provides us the ability to realize better results with less overhead. By using Comprehend Medical with our customers we are able to focus more on building smarter applications and less on annotating, training and re-training models,” said Matt Rich, Healthcare AI Lead for PwC. “For our customers the ability to perform a very manual task accurately at scale allows us to create more impactful solutions. For example, one of our pharmaceutical clients is using Comprehend Medical on a limited sample size to help extract information that allows them to identify medically relevant events. In preliminary findings, we are seeing a significantly faster throughput than in the past.”

Domino’s Pizza Enterprises Ltd. is the largest pizza chain in Australia and their vision is to be the leader in the Internet of Food in every neighborhood. “The customer is at the heart of everything we do at Domino’s and we are working relentlessly to improve and enhance their experience with the brand,” said Mallika Krishnamurthy, Global Head, Strategy & Insights, Domino’s Pizza Enterprises. “Using Amazon Personalize in conjunction with Amazon Pinpoint, we are able to achieve personalization at scale across our entire customer base, which was previously impossible. Amazon Personalize enables us to apply context about individual customers and their circumstances, and deliver customized communications such as special deals and offers through our digital channels.”

Mercado Libre is a leading online commerce and payments platform in Latin America. “We have been predicting demand for over 50,000 different products using Amazon Forecast’s state-of-the-art deep learning algorithms, that we can use right out of the box,” said Adrian Quilis, Director of Business Intelligence for Mercado Libre. “Amazon Forecast takes care of all the heavy lifting of setting up pipelines, re-training schedules, and re-generating forecasts, so we can experiment with hundreds of models very easily.”

About Amazon Web Services

For over 12 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 125 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 57 Availability Zones (AZs) within 19 geographic regions around the world, spanning the US, Australia, Brazil, Canada, China, France, Germany, India, Ireland, Japan, Korea, Singapore, and the UK. AWS services are trusted by millions of active customers around the world—including the fastest-growing startups, largest enterprises, and leading government agencies—to power their infrastructure, make them more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

About Amazon

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.

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CONTACT: Amazon.com, Inc.

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SOURCE: Amazon Web Services, Inc.

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