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 1/18th 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/
“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.
- New Amazon Elastic Compute Cloud (EC2) GPU instances (available next week): With eight NVIDIA V100 GPUs, 32GB GPU memory, fast NVMe storage, 96 Intel “Skylake” vCPUs, and 100Gbps networking, the new P3dn.24xl instances are the most powerful machine learning training processors available in the cloud, allowing developers to train models with more data in less time.
- AWS-Optimized TensorFlow framework (generally available today): When training with large amounts of data, developers who choose to use TensorFlow have found that it’s challenging to scale TensorFlow across many GPUs, which often results in low utilization of these GPUs and longer training times for large training jobs. AWS worked on this problem and has innovated on how to make TensorFlow scale across GPUs. By improving the way in which TensorFlow distributes training tasks across those GPUs, the new AWS-Optimized TensorFlow achieves close to linear scalability when training multiple types of neural networks (90 percent efficiency across 256 GPUs, compared to the prior norm of 65 percent). Using the new AWS-Optimized TensorFlow and P3dn instances, developers can now train the popular ResNet-50 model in only 14 minutes, the fastest time recorded, and 50 percent faster than the previous best time. And, these optimizations are generally applicable not just for computer vision models but also for a broader set of deep learning models.
- Amazon Elastic Inference (generally available today): While training rightfully receives a lot of attention, inference actually accounts for the majority of the cost and complexity for running machine learning in production (for every dollar spent on training, nine are spent on inference). Amazon Elastic Inference allows developers to dramatically decrease inference costs with up to 75 percent savings when compared to the cost of using a dedicated GPU instance. Instead of running on a whole Amazon EC2 P2 or P3 instance with relatively low utilization, developers can run on a smaller, general-purpose Amazon EC2 instance and provision just the right amount of GPU performance from Amazon Elastic Inference. Starting at just 1 TFLOP, developers can elastically increase or decrease the amount of inference performance, and only pay for what they use. Elastic Inference supports all popular frameworks, and is integrated with Amazon SageMaker and the Amazon EC2 Deep Learning Amazon Machine Image (AMI). And, developers can start using Amazon Elastic Inference without making any changes to their existing models.
- AWS Inferentia (available in 2019): For larger workloads that consume entire GPUs or require lower latency, AWS announced a high performance machine learning inference chip, custom designed by AWS. AWS Inferentia provides hundreds of teraflops per chip and thousands of teraflops per Amazon EC2 instance for multiple frameworks (including TensorFlow, Apache MXNet, and PyTorch), and multiple data types (including INT-8 and mixed precision FP-16 and bfloat16).
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 1/18th 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.
- Amazon SageMaker Ground Truth (generally available today): The journey to build machine learning models requires developers to prepare their datasets for training their ML models.Before developers can select their algorithms, build their models, and deploy them to make predictions, human annotators manually review thousands of examples and add the labels required to train machine learning models. This process is time consuming and expensive. Amazon SageMaker Ground Truth makes it much easier for developers to label their data using human annotators through Mechanical Turk, third party vendors, or their own employees. Amazon SageMaker Ground Truth learns from these annotations in real time and can automatically apply labels to much of the remaining dataset, reducing the need for human review. Amazon SageMaker Ground Truth creates highly accurate training data sets, saves time and complexity, and reduces costs by up to up to 70 percent when compared to human annotation.
- AWS Marketplace for Machine Learning (generally available today): Machine learning is moving quickly, with new models and algorithms from academia and industry appearing virtually every week. Amazon SageMaker includes some of the most popular models and algorithms built-in, but to make sure developers continue to have access to the broadest set of capabilities, the new AWS Marketplace for Machine Learning includes over 150 algorithms and models (with more coming every day) that can be deployed directly to Amazon SageMaker. Developers can start using these immediately from SageMaker. Adding a listing to the Marketplace is completely self-service for developers who want to sell through AWS Marketplace.
- Amazon SageMaker RL (generally available today): In machine learning circles, there is a lot of buzz about reinforcement learning because it’s an exciting technology with a ton of potential. Reinforcement learning trains models, without large amounts of training data, and it’s broadly useful when the reward function of a desired outcome is known but the path to achieving it is not and requires a lot of iteration to discover. Healthcare treatments, optimizing manufacturing supply chains, and solving gaming challenges are a few of the areas that reinforcement learning can help address. However, reinforcement learning has a steep learning curve and many moving parts, which effectively puts it out of the reach of all but the most well-funded and technical organizations. Amazon SageMaker RL, the cloud’s first managed reinforcement learning service, allows any developer to build, train, and deploy with reinforcement learning through managed reinforcement learning algorithms, support for multiple frameworks (including Intel Coach and Ray RL), multiple simulation environments (including SimuLink and MatLab), and integration with AWS RoboMaker, AWS’s new robotics service, which provides a simulation platform that integrates well with SageMaker RL.
- AWS DeepRacer (available for pre-order today): In just a few lines of code, developers can start learning about reinforcement learning with AWS DeepRacer, a 1/18th scale fully autonomous race car. The car (with all-wheel drive, monster truck tires, an HD video camera, and on-board compute) is driven using reinforcement learning models trained using Amazon SageMaker. Developers can put their skills to the test and race their cars and models against other developers for prizes and glory in the DeepRacer League, the world’s first global autonomous racing league, open to everyone.
- Amazon SageMaker Neo (generally available today): The new deep learning model compiler lets customers train models once, and run them anywhere with up to 2X improvement in performance. Applications running on connected devices at the edge are particularly sensitive to performance of machine learning models. They require low latency decisions, and are often deployed across a broad number of different hardware platforms. Amazon SageMaker Neo compiles models for specific hardware platforms, optimizing their performance automatically, allowing them to run at up to twice the performance, without any loss in accuracy. As a result, developers no longer need to spend time hand tuning their trained models for each and every hardware platform (saving time and expense). SageMaker Neo supports hardware platforms from NVIDIA, Intel, Xilinx, Cadence, and Arm, and popular frameworks such as TensorFlow, Apache MXNet, and PyTorch. AWS will also make Neo available as an open source project.
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. AmazonSageMaker 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.
- Amazon Textract (available in preview today): Many companies today extract data from documents and forms through manual data entry which is slow and expensive, or using simple optical character recognition (OCR) software, which is often inaccurate and typically produces output that requires extensive post-processing to put the extracted content in a format that is usable by a developer’s application. Amazon Textract uses machine learning to instantly read virtually any type of document to accurately extract text and data without the need for any manual review or custom code. Amazon Textract allows developers to quickly automate document workflows, processing millions of document pages in a few hours.
- Amazon Comprehend Medical (generally available today): Building the next generation of medical applications requires being able to understand and analyze the information that is often trapped in free-form, unstructured medical text, such as hospital admission notes or patient medical histories. Comprehend Medical is a highly accurate natural language processing service for medical text, which uses machine learning to extract disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other electronic health records. Comprehend Medical requires no machine learning expertise, no complicated rules to write, no models to train, and it is continuously improving. You pay only for what you use and there are no minimum fees or upfront commitments.
- Amazon Personalize (available in preview today): Based on the same technology that powers Amazon.com, Amazon Personalize is a real-time recommendation and personalization service. Amazonpioneered the use of machine learning for recommendation and personalization over twenty years ago. Experience has shown that there is no master algorithm for personalization. Each use case, from videos, music, products, news articles, has its own specificities, which require a unique mix of data, algorithms, and optimizations. Amazon Personalize provides this experience to customers in a fully managed service, which will build, train, and deploy custom, private personalization and recommendation models for virtually any use case. Amazon Personalize can make recommendations, personalize search results, and segment customers for direct and personalized marketing through email or push notifications.
- Amazon Forecast (available in preview today): Just like personalization,forecasting has traditionally been a bit of a dark art, where customers try to predict future trends in supply chain, inventory levels, and product demand, based on historical data. And just like Amazon Personalize, Amazon Forecast is based on technology that has been developed by Amazon.com and used for a lot of critical forecasting. Forecasting is hard to do well because there are often so many inter-related factors (such as pricing, events, and even the weather). Missing the mark with a forecast can have a significant impact, such as being unable to meet customer demand or significantly over-spending. Amazon Forecast creates accurate time-series forecasts. Using historical data and related causal data, Amazon Forecast will automatically train, tune, and deploy custom, private machine learning forecasting models, so that customers can be more confident that they’ll provide the right customer experience while optimizing their spend.
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.”
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