At AWS Re:Invent, Amazon Web Services, Inc. has announced five new machine learning services and a deep learning-enabled wireless video camera for developers.
Amazon SageMaker is a fully managed service for developers and data scientists to quickly build, train, deploy, and manage their own machine learning models.
AWS also introduced AWS DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning.
And, AWS announced four new application services that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time.
“Our original vision for AWS was to enable any individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world. Our vision for machine learning is no different,” said Swami Sivasubramanian, VP of Machine Learning, AWS. “We want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level. Amazon SageMaker removes a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models.”
With Amazon SageMaker developers can:
Easily build machine learning models with performance-optimized algorithms: Amazon SageMaker is a fully managed machine learning notebook environment makes it easy for developers to explore and visualize data they have stored in Amazon Simple Storage Service (Amazon S3), and transform it using all of the popular libraries, frameworks, and interfaces.
Amazon SageMaker includes ten of the most common deep learning algorithms (e.g. k-means clustering, factorization machines, linear regression, and principal component analysis), which AWS has optimized to run up to ten times faster than standard implementations.
Developers simply choose an algorithm and specify their data source, and Amazon SageMaker installs and configures the underlying drivers and frameworks. Amazon SageMaker includes native integration with TensorFlow and Apache MXNet with additional framework support coming soon. Developers can also specify any framework and algorithm they choose by uploading them into a container on the Amazon EC2 Container Registry.
Fast, fully managed training: Amazon SageMaker makes training easy. Developers simply select the type and quantity of Amazon EC2 instances and specify the location of their data. Amazon SageMaker sets up the distributed compute cluster, performs the training, outputs the result to Amazon S3, and tears down the cluster when complete.
Amazon SageMaker can automatically tune models with hyper-parameter optimization, adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions.
Deploy models into production with one click: Amazon SageMaker takes care of launching instances, deploying the model, and setting up a secure HTTPS end-point for the application to achieve high throughput and low latency predictions, as well as auto-scaling Amazon EC2 instances across multiple availability zones (AZs).
It also provides native support for A/B testing. Once in production, Amazon SageMaker eliminates the heavy lifting involved in managing machine learning infrastructure, performing health checks, applying security patches, and conducting other routine maintenance.
With AWS DeepLens, developers can:
Get hands-on machine learning experience: AWS DeepLens is the first of its kind: a deep-learning enabled, fully programmable video camera, designed to put deep learning into the hands of any developer, literally. AWS DeepLens includes a HD video camera with on-board compute capable of running sophisticated deep learning computer vision models in real-time.
The custom-designed hardware, capable of running over 100 billion deep learning operations per second, comes with sample projects, example code, and pre-trained models so even developers with no machine learning experience can run their first deep learning model in less than ten minutes.
Developers can extend these tutorials to create their own custom, deep learning-powered projects with AWS Lambda functions. For example, AWS DeepLens could be programmed to recognize the numbers on a license plate and trigger a home automation system to open a garage door, or AWS DeepLens could recognize when the dog is on the couch and send a text to its owner.
Train models in the cloud and deploy them to AWS DeepLens: AWS DeepLens integrates with Amazon SageMaker so that developers can train their models in the cloud with Amazon SageMaker and then deploy them to AWS DeepLens with just a few clicks in the AWS Management Console. The camera runs the models, in-real time, on the device.
“We’ve deepened our relationship with AWS, adding them as an Official Technology Provider of the NFL and are excited to use Amazon SageMaker for our next-generation stats initiative,” said Michelle McKenna-Doyle, SVP and CIO, National Football League. “With Amazon SageMaker in our toolkit, our developers can stop worrying about the undifferentiated heavy lifting of machine learning, and start adding new visualizations, stats, and experiences that our fans will adore.”
As the world’s leading provider of high-resolution Earth imagery, data and analysis, DigitalGlobe works with enormous amounts of data every day. “DigitalGlobe is making it easier for people to find, access, and run compute against our 100PB image library which is stored in the AWS cloud in order to apply deep learning to satellite imagery,” said Dr. Walter Scott, Chief Technology Officer of Maxar Technologies and founder of DigitalGlobe. “We plan to use Amazon SageMaker to train models against petabytes of earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe’s Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale.”
Hotels.com is a leading global lodging brand operating 90 localized websites in 41 languages, “At Hotels.com, we are always interested in ways to move faster, to leverage the latest technologies and stay innovative,” says Matt Fryer, VP and Chief Data Science Officer of Hotels.com and Expedia Affiliate Network. “With Amazon SageMaker, the distributed training, optimized algorithms, and built-in hyperparameter features should allow my team to quickly build more accurate models on our largest data sets, reducing the considerable time it takes us to move a model to production. It is simply an API call. Amazon SageMaker will significantly reduce the complexity of machine learning, enabling us to create a better experience for our customers, fast.”
Intuit recognizes the enormous value and power of machine learning to help its customers make better decisions and streamline their work, every day. “With Amazon SageMaker, we can accelerate our artificial intelligence initiatives at scale by building and deploying our algorithms on the platform,” says Ashok Srivastava, Chief Data Officer at Intuit. “We will create novel large-scale machine learning and AI algorithms and deploy them on this platform to solve complex problems that can power prosperity for our customers.”
Thomson Reuters is the world’s leading source of news and information for professional markets. “For over 25 years we have been developing advanced machine learning capabilities to mine, connect, enhance, organize and deliver information to our customers, successfully allowing them to simplify and derive more value from their work,” said Khalid Al-Kofahi, who leads Thomson Reuters center for AI and Cognitive Computing. “Working with Amazon SageMaker enabled us to design a natural language processing capability in the context of a question-answering application. Our solution required several iterations of deep learning configurations at scale using the capabilities of Amazon SageMaker.”
“Deep learning is something that our students find really inspiring. It seems like every week now it is leading to new breakthroughs in robotics, language, and biology. What I like about AWS DeepLens is that it seems likely to democratize access to experimenting with machine learning,” said Andrew Moore, Dean of the School of Computer Science at Carnegie Mellon University. “Campuses like ours are going to be really excited to bring AWS DeepLens into our classrooms and labs to help accelerate the process of getting students into real-world deep learning.”
“At Isentia, we built our media intelligence software in a single language. To expand our capabilities and address the diverse language needs of our customers, we needed translation support to generate and deliver valuable insights from non-English media content. Having tried multiple machine translation services in the past, we are impressed with how easy it is to integrate Amazon Translate into our pipeline and its ability to scale to handle any volume we throw at it. The translations also came out more accurate and nuanced and met our high standards for clients,” says Andrea Walsh, CIO at Isentia.
“RingDNA is an end-to-end communications platform for sales teams. Hundreds of enterprise organizations use RingDNA to dramatically increase productivity, engage in smarter sales conversations, gain predictive sales insights, improve their win rate and coach reps to succeed faster than ever before. A critical component of RingDNA’s Conversation AI requires best of breed speech-to-text to deliver transcriptions of every phone call. RingDNA is excited about Amazon Transcribe since it provides high-quality speech recognition at scale, helping us to better transcribe every call to text,” said Howard Brown, CEO and Founder at RingDNA.
“Building intelligent applications to help customers drive their businesses is our entire focus,” said Manjunath Ganimasty, V.P. Software Development with Infor. “Amazon Comprehend allows us to analyze unstructured text within search, chat, and documents to understand intent and sentiment. This capability enables us to train our Coleman AI skillset, and also provide a truly focused and tailored search experience for our customers.”
“Natural language processing is hard. We’ve looked at everything from closed to open-source solutions to analyze and make sense of our data, but couldn’t find a practical solution that would allow us to stay agile, scalable, and cost effective. Amazon Comprehend provides a continuously-trained model allowing us to focus on our business and innovate in Supply Chain Management (SCM),” said Minh Chau, Head of Engineering at Elementum.
“The City of Orlando is excited to work with Amazon to pilot the latest in public safety software through a unique, first-of-its-kind public-private partnership,” said John Mina Police Chief., City of Orlando. “Through the pilot, Orlando will utilize Amazon’s Rekognition Video and Acuity technology in a way that will use existing City resources to provide real-time detection and notification of persons-of-interest, further increasing public safety and operational efficiency opportunities for the City of Orlando and other cities across the nation. ”
“The analytic features of Amazon Rekognition Video are impressive. They can, for example, help with search of historical and real time video for persons-of-interest, providing efficiencies and awareness by automating this typically human task,” Dan Law, Chief Data Scientist at Motorola.
