Amazon Web Services, (AWS), an Amazon.com company, shared that tens of thousands of customers are using AWS machine learning services, with active users increasing more than 250 percent in the last year, spurred by the broad adoption of Amazon SageMaker since AWS re: Invent 2017. Amazon SageMaker is a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to use machine learning much more expansively and successfully. AWS has meaningfully more reference customers for machine learning than any other provider, and much of it has to do with AWS’s unmatched array of services that enable a full stack machine learning experience. With AWS machine learning services, customers are building a wide variety of intelligent applications and solutions with the help of AWS’s P2 and P3 graphical processing unit (GPU) instances, deep learning Amazon Machine Images (AMIs) that embed all the major frameworks, Amazon SageMaker, AWS DeepLens—a device that has helped thousands of customers gain hands on experience with machine learning, and services at the top layer of the stack such as Amazon Rekognition, Amazon Polly, Amazon Lex, and Amazon Comprehend.
AWS also announced the general availability of two new machine learning services, which are part of AWS’s machine learning portfolio, Amazon Transcribe and Amazon Translate. Amazon Transcribe provides grammatically correct transcriptions of audio files to allow audio data to be analyzed, indexed, and searched. Amazon Translate is a deep learning powered machine translation service that provides natural sounding language translation in both real-time and batch scenarios. These services further extend the language capabilities already provided on AWS with Amazon Lex for conversational interfaces, Amazon Polly for Text-to-Speech, and Amazon Comprehend for processing natural language to discover insights and contextual relationships in text.
“A lot of companies are talking about the potential of machine learning and artificial intelligence, and thinking about how to incorporate these technologies in their applications, but in reality, machine learning has been out of reach for all but the few organizations who have expert practitioners and data scientists on staff,” said Swami Sivasubramanian, Vice President of Machine Learning at AWS. “AWS changed all this with the introduction of Amazon SageMaker that makes machine learning accessible to everyday developers by eliminating the heavy lifting of building, training, and deploying models.”
Sivasubramanian continued, “More companies are doing machine learning on AWS than anywhere else—at every layer of the stack. From those who are super comfortable with machine learning using their favorite frameworks with our high performance P3 instances, to everyday developers incorporating machine learning into their applications for the first time using Amazon SageMaker, to developers leveraging voice, text, video, translation, facial recognition, and audio transcription to invent new customer experiences using AWS’s artificial intelligence services.”
Articulate, Cathay Pacific, Cerner, Cookpad, Cox Automotive, DailyLook, DigitalGlobe, Dow Jones, Echo360, Edmunds.com, Enetpulse, Expedia.com, FamilySearch, FICO, GE Healthcare, Genesys, Grammarly, Intuit, KloudGin, Lau Brothers, Limbik, Lionbridge, NFL, One Hour Translation, Polotico.eu, POPSUGAR, PubNub, Realtor.com, RedAwning.com, Shutterfly, TINT, Tinder, VidMob, VMWare, and ZipRecruiter are just a few of the tens of thousands of customers using AWS machine learning technologies to reimagine customer experiences and innovate across their businesses.
Harnessing data and analytics across hardware, software, and biotech, GE Healthcare is transforming healthcare by delivering better outcomes for providers and patients. “Amazon SageMaker allows GE Healthcare to access powerful artificial intelligence tools and services to advance improved patient care,” said Sharath Pasupunuti, Artificial Intelligence Engineering Leader at GE Healthcare. “The scalability of Amazon SageMaker, and its ability to integrate with native AWS services, adds enormous value for us. We are excited about how our continued collaboration between the GE Health Cloud and Amazon SageMaker will drive better outcomes for our healthcare provider partners and deliver improved patient care.”
An early enterprise AWS customer, Intuit is a financial technology company that is committed to powering prosperity around the world for consumers, small businesses, and the self-employed through its ecosystem of global products and platforms. “By including AWS machine learning and artificial intelligence workloads in our overall artificial intelligence and machine learning strategy, we can accelerate the end-user benefits within our flagship products like QuickBooks, Mint, and TurboTax,” said H. Tayloe Stansbury, Intuit’s Executive Vice President and Chief Technology Officer. “Intuit started our artificial intelligence journey over ten years ago and are proud that we have over 150 patents and 40 systems in production in this area, and we look forward to continue innovating to delight our customers.”