Tens of thousands of customers flocking to AWS for machine learning services

Amazon Web Services, Inc. (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.

Today, 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 AmazonSageMaker, 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.”

Edmunds.com is a car-shopping website that offers detailed, constantly updated information about vehicles to 20 million monthly visitors. “We have a strategic initiative to put machine learning into the hands of all our engineers,” said Stephen Felisan, Chief Information Officer atEdmunds.com. “Amazon SageMaker is key to helping us achieve this goal, making it easier for engineers to build, train, and deploy machine learning models and algorithms at scale. We are excited to see how we can use Amazon SageMaker to innovate new solutions across the organization for our customers.”

The Move, Inc. network, which includes Realtor.com, Doorsteps, and Moving.com, provides real estate information, tools, and professional expertise across a family of websites and mobile experiences for consumers and real estate professionals. “We believe that Amazon SageMaker is a transformative addition to the realtor.com toolset as we support consumers along their homeownership journey,” said Vineet Singh, Chief Data Officer and Senior Vice President at Move, Inc. “Machine learning workflows that have historically taken a long time, like training and optimizing models, can be done with greater efficiency and by a broader set of developers, empowering our data scientists and analysts to focus on creating the richest experience for our users.”

Dow Jones is a publishing and financial information firm that publishes the world’s most trusted business news and financial information in a variety of media. It delivers breaking news, exclusive insights, expert commentary and personal finance strategies. “As Dow Jones continues to focus on integrating machine learning into our products and services, AWS has been a great resource,” said Ramin Beheshti, Group Chief Product and Technology Officer. “Leading up to our recent Machine Learning Hackathon, the AWS team provided training to participants on Amazon SageMaker and Amazon Rekognition, and offered day-of support to all the teams. The result was that our teams developed some great ideas for how we can apply machine learning, many of which we we’ll continue to develop on AWS. The event was a huge success, and an example of what a great relationship can look like.”

Every day Grammarly’s algorithms help millions of people communicate more effectively by offering writing assistance on multiple platforms across devices. Through a combination of natural language processing and advanced machine learning technologies, Grammarly is tackling critical communication and business challenges. “Amazon SageMaker makes it possible for us to develop our TensorFlow models in a distributed training environment,” said Stanislav Levental, Technical Lead at Grammarly. “Our workflows also integrate with Amazon EMR for pre-processing, so we can get our data from Amazon Simple Storage Service (Amazon S3), filtered with Amazon EMR and Spark from a Jupyter notebook, and then train in Amazon SageMaker with the same notebook. Amazon SageMaker is also flexible for our different production requirements. We can run inferences on Amazon SageMaker itself, or if we need just the model, we download it from Amazon S3 and run inferences of our mobile device implementations for iOS and Android customers.”