Customer Use Cases
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. 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.”
- Matt Fryer, VP and Chief Data Science Officer of Hotels.com and Expedia Affiliate Network
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. 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 Amazon SageMaker's capabilities.”
- Khalid Al-Kofahi, Thomson Reuters Center for AI and Cognitive Computing
Intuit is a business and financial software company that develops and sells financial, accounting and tax preparation software and related services for small businesses, accountants and individuals.
“With Amazon SageMaker, we can accelerate our Artificial Intelligence initiatives at scale by building and deploying our algorithms on the platform. 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.”
- Ashok Srivastava, Chief Data Officer at Intuit
As the world’s leading provider of high-resolution Earth imagery, data and analysis, DigitalGlobe works with enormous amounts of data every day.
“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 entire 100PB image library, which is stored in AWS’s cloud, to apply deep learning to satellite imagery. 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.”
- Dr. Walter Scott, Chief Technology Officer of Maxar Technologies and founder of DigitalGlobe
Dow Jones & Co. is a global provider of news and business information, delivering content to consumers and organizations via newspapers, Web sites, mobile apps, video, newsletters, magazines, proprietary databases, conferences, and radio.
“As Dow Jones continues to focus on integrating machine learning into our products and services, AWS has been a great partner. 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 partnership can look like.”
- Ramin Beheshti, Group Chief Product and Technology Officer at Dow Jones
Cookpad is Japan’s largest recipe sharing service, with about 60 million monthly users in Japan and about 90 million monthly users globally.
“With the increasing demand for easier use of Cookpad’s recipe service, our data scientists will be building more machine learning models in order to optimize the user experience. Attempting to minimize the number of training job iterations for best performance, we recognized a significant challenge in the deployment of ML inference endpoints, which was slowing down our development processes. To automate the ML model deployment such that data scientists could deploy models by themselves, we used Amazon SageMaker’s inference APIs and proved that Amazon SageMaker would eliminate the need for application engineers to deploy ML models. We anticipate automating this process with Amazon SageMaker in production.”
- Yoichiro Someya, Research Engineer at Cookpad
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.
“Amazon SageMaker makes it possible for us to develop our TensorFlow models in a distributed training environment. Our workflows also integrate with Amazon EMR for pre-processing, so we can get our data from Amazon S3, filtered with EMR and Spark from a Jupyter notebook, and then train in Amazon SageMaker with the same notebook. SageMaker is also flexible for our different production requirements. We can run inferences on SageMaker itself, or if we need just the model, we download it from S3 and run inferences of our mobile device implementations for iOS and Android customers.”
- Stanislav Levental, Technical Lead at Grammarly
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. 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."
- Vineet Singh, Chief Data Officer and Senior Vice President at Move, Inc.
With 20 billion matches to date, Tinder is the world's most popular app for meeting new people.
“Behind every Tinder swipe is a system that manages millions of requests a minute, billions of swipes a day, across more than 190 countries. Amazon SageMaker simplifies machine learning, helping our development teams to build models for predictions that create new connections that otherwise might have never been possible.”
- Elie Seidman, Chief Executive Officer of Tinder
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 of our engineers. 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 Edmunds will use SageMaker to innovate new solutions across the organization for our customers.”
- Stephen Felisan, Chief Information Officer at Edmunds.com
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.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.”
- Sharath Pasupunuti, AI Engineering Leader, GE Healthcare
Zendesk builds software for better customer relationships. It empowers organizations to improve customer engagement and better understand their customers. More than 94,000 paid customer accounts in over 150 countries and territories use Zendesk products.
"Amazon SageMaker will lower our costs and increase velocity for our use of machine learning. With Amazon SageMaker, we can transition from our existing self-managed TensorFlow deployment to a fully-managed service. Amazon SageMaker also gives us easier access to other popular deep-learning frameworks, while managing the infrastructure for authoring, training and serving our models."
- David Bernstein, Director of Strategic Technology, Zendesk
Atlas Van Lines is the second largest van line in North America, formed in 1948 by a group of entrepreneurs in the moving and storage industry. The organization was developed with the single goal of moving from coast to coast while adhering to the golden rule of business. In addition to a robust footprint, Atlas boasts stringent agent quality requirements that surpass that of the industry.
During peak moving seasons, the Atlas agent network works together across markets to meet customer demand. Traditionally, their ability to forecast capacity was manual and labor intensive. They relied on the wisdom and gut instinct of resources with many years of experience. Atlas had the historical data from 2011 forward and desired to find a way to dynamically adjust capacity and price based on future market demands.
Atlas worked with Pariveda Solutions, an APN Premier Consulting Partner, to help unlock the possibility of proactive capacity and price management in the long-haul moving industry. Pariveda prepared the data, developed, and evaluated the Machine Learning model, and tuned the performance. They used Amazon SageMaker to train and optimize the model, and then exported it using Amazon SageMaker’s modular nature to run using Amazon EC2.
Formerly Motoring.co.uk, Regit is an automotive tech firm and the UK’s leading online service for motorists. They deliver digital car management services based on a car’s registration plate, and provide drivers with informative reminders such as Ministry of Transport (MOT) tax, insurance, and recalls.
Regit worked with Peak Business Insight, an APN Advanced Consulting Partner, to apply “Categorical Machine Learning models” that handle both category and variable data simultaneously to give predictions about the likelihood of users changing cars, resulting in a sale for Regit.
Peak used AWS services such as Amazon SageMaker for real-time ingestion, modeling, and data output. Amazon SageMaker handles 5,000 API requests a day for Regit, seamlessly scaling and adjusting to relevant data requirements and managing the delivery of lead scoring results. Meanwhile, Amazon Redshift and Amazon Elastic Compute Cloud (Amazon EC2) instances efficiently and continuously optimize model performance and results. With Peak, Regit has been able to predict which of its 2.5 million users are going to change cars and when. This means they can serve customers in a more personalized and targeted way, increasing call center revenues by more than a quarter.
As competitive athletes in several disciplines, Sportograf has a natural affinity for sports. Their mission is to respect and honor the performance of every athlete with professional quality pictures.
“With millions of pictures generated from sporting events, our challenge was to organize photos by bib number with high speed and accuracy. In searching for a solution, Sportograf decided not to work with special QR codes or other markers since they introduce a large and complex workload, making it impossible to respond to spontaneous customer requests. To solve this challenge, Amazon Rekognition for text recognition, and Amazon SageMaker enabled us to build our own Machine Learning solution to further identify the racers’ bib-numbers, in near real-time.”
– Tom Janas, Managing Director, Sportograf
Major League Baseball (MLB) is the most historic professional sports league in the United States and consists of 30 member clubs in the U.S. and Canada, representing the highest level of professional baseball. Statcast is a state-of-the-art tracking technology introduced by MLB that allows for the collection and analysis of a massive amount of baseball data, in ways that were never possible in the past.
AWS’s broad range of cloud-based machine learning services will enable MLB to eliminate the manual, time-intensive processes associated with record keeping and statistics, such as scorekeeping, capturing game notes, and classifying pitches. By using Amazon SageMaker, MLB is empowering its developers and data scientists to automate these tasks as they learn to quickly and easily build, train, and deploy machine learning models at scale.
MLB and Amazon ML Solutions Lab are using Amazon SageMaker to test how well they can accurately predict pitches by evaluating the pitcher, batter, catcher, and situation to predict the type and location of the next pitch. MLB also intends to leverage Amazon SageMaker and the natural language processing service Amazon Comprehend to build a language model that would create analysis for live games in the tone and style of iconic announcers to capture that distinct broadcast essence baseball fans know and revere.
Celgene is a global biopharmaceutical company committed to improving the lives of patients worldwide. The focus is on the discovery, development, and commercialization of innovative therapies for patients with cancer, immune-inflammatory, and other unmet medical needs.
“At Celgene, our vision is to deliver truly innovative and life-changing treatments and improve the lives of patients worldwide. With Amazon SageMaker and Apache MXNet, building and training deep learning models to develop solutions and processes has been quicker and easier than before and we’re able to easily scale our efforts to discover treatments and produce drugs. Using SageMaker and Amazon EC2 P3 instances has accelerated our time-to-train models and productivity, allowing our team to focus on groundbreaking research and discovery.”
– Lance Smith, Director, Celgene
Zocdoc provides medical care search for end users with an integrated solution about information on medical practices and individual doctor schedules. The focus is on patient needs and to deliver the best healthcare experience.
“At Zocdoc, our focus has been to make it easy for patients to find the right doctor and book an appointment at the most convenient time and location. There is a lot of excitement among Zocdoc engineers around how easy it is to quickly build, train, and deploy models using Amazon SageMaker. One of our mobile engineers was able to train and deploy a doctor specialty recommendation model from scratch in less than a day, which we ended up rolling out to production. Previously, our data science team had to contribute to the development of any model work, which slowed down product teams. With Amazon SageMaker, we can get this from concept to production much faster, due to the ease of streamlined end-to-end capabillities of SageMaker."