See how leading organizations worldwide are using Amazon SageMaker to build, train, and deploy machine learning models.
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 - Intuit
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
Major League Baseball
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.
“Incorporating machine learning into our systems and practices is a great way to take understanding of the game to a whole new level for our fans and the 30 clubs. We chose AWS because of their strength, depth, and proven expertise in delivering machine learning services and are looking forward to working with the Amazon ML Solutions Lab on a number of exciting projects, including detecting and automating key events, as well as creating new opportunities to share never-before-seen metrics.”
Jason Gaedtke, Chief Technology Officer - Major League Baseball
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 - Dow Jones
ProQuest curates the world’s largest collection of journals, ebooks, primary sources, dissertations, news, and video – and builds powerful workflow solutions to help libraries acquire and grow their collections. ProQuest products and services are used in academic, K-12, public, corporate and government libraries in 150 countries.
“We are collaborating with AWS to build a more appealing video user experience for library patrons, enabling their searches to return more relevant results. By working with the AWS ML Solutions Lab, we tested different algorithms using Amazon SageMaker, tuned the models using hyperparameter optimization, and automated the deployment of machine learning (ML) models. We are pleased with the results thus far and are currently considering ML technologies for other products.”
Allan Lu, Vice President of Research Tools, Services & Platforms - ProQuest
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
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, AI and Cognitive Computing - Thomson Reuters Center
Atlas Van Lines
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.
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 - 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 - Edmunds.com
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 - Hotels.com and Expedia Affiliate Network
Formosa Plastics Corporation is a growing, vertically-integrated supplier of plastic resins and petrochemicals. Formosa Plastics offers a full line of polyvinyl chloride, polyethylene and polypropylene resins, caustic soda and other petrochemicals that deliver the consistency, performance and quality that customers demand.
"Formosa Plastics is one of Taiwan’s top petrochemical companies and ranks among the world's leading plastics manufacturers. We decided to explore Machine Learning to enable more accurate detection of defects and reduce manual labor costs, and we turned to AWS as our preferred cloud provider to help us do that. The AWS ML Solutions Lab worked with us through every step of the process, from a discovery workshop to define the business use cases to the building and selection of appropriate ML models to the actual deployment. Using Amazon SageMaker, the machine learning solution reduced our employee time spent doing manual inspection in half. With the Solutions Lab’s help, we are now able to optimize the SageMaker model ourselves going forward as conditions change.”
Bill Lee, Assistant Vice President - Formosa Plastics Corporation
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
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.
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."
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 - Move, Inc.
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 - Grammarly
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 - Maxar Technologies and founder of DigitalGlobe
Intercom’s messaging-first products integrate seamlessly with other companies' websites and mobile apps to help them acquire, engage, and support customers. Founded in 2011, the company has offices in San Francisco, London, Chicago, and Dublin.
“At Intercom, we have a growing team of data scientists and data-oriented engineers, and we often want to iterate quickly and explore new solutions for data-driven products. Prior to Amazon SageMaker, we tried a lot of different options to build these products but each one came with challenges – code sharing was hard, testing on big datasets was slow, and provisioning and managing hardware on our own was problematic. SageMaker came along and solved all that for us. We use it in particular to develop algorithms for our search platforms and machine learning features, and we find SageMaker's hosted Jupyter Notebooks allows us to build and iterate rapidly. Crucially, the fact that SageMaker is a managed service allows my team to focus on the task at hand. Amazon SageMaker is an extremely valuable service to us at Intercom, and we're excited to continue using more and more as our company grows."
Kevin McNally, Senior Data Scientist Machine Learning - Intercom
Kinect Energy Group
Kinect Energy Group is a subsidiary of World Fuel Services, a Fortune 100 company that provides energy procurement advisory services, supply fulfillment, and transaction and payment management solutions to commercial and industrial customers, principally in the aviation, marine and land transportation industries. Kinect Energy is a key Nordic energy provider and is dependent on the natural power resources enabled by the region’s windy climate.
The business has recently catapulted forward with the introduction of a number of AI / ML services from AWS. With Amazon SageMaker, the company can predict the upcoming weather trends and therefore the prices of future months’ electricity, enabling unprecedented long-range energy trading that represents an industry-leading forward-thinking approach.
“We started using Amazon SageMaker and with the help of the AWS ML Solutions Team and the Solutions Architecture Team, we picked up momentum with Innovation Day and the impact has been tremendous ever since. We’ve grown our own AI team several times to fully exploit the new advantage that AWS’ technologies provide. We’re profiting in new ways by setting prices based on weather that hasn’t yet happened. We’ve gone “all in” with AWS, including storing our data in S3, using Lamda for execution, and step functions in addition to SageMaker. And, thanks to the AWS ML Solutions Lab’s committed partnership, we are now self-sufficient, able to iterate on the models we’ve built and continue improving our business.”
Andrew Stypa, Lead Business Analyst - Kinect Energy Group
Frame.io is your hub for all things video. The leader in video review and collaboration with 700K+ customers globally, Frame.io is where video professionals across the entire spectrum — from freelance to enterprise — come to review, approve and deliver video.
“As a cloud-native video review and collaboration platform accessible to users all over the world, it's imperative we provide best-in-class security to our customers. With the anomaly detection model built into Amazon SageMaker, we are able to leverage machine learning to quickly identify, detect, and block any unwanted IP requests to ensure our client's media remains secure and protected at all times. Getting started with Amazon SageMaker, maintaining it over time, scaling it across our platform, and adjusting to our specific workflows has been simple and straight-forward. And, with the help of Jupyter notebooks in SageMaker, we've been able to experiment with different models to improve our precision and recall in ways that make Frame.io even more secure.”
Abhinav Srivastava, VP and Head of Information Security - Frame.io
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
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 - Cookpad
Fabulyst is an India-based startup focusing on fashion commerce that enables more positive and personalized experiences for shoppers and better conversions for retailers through AI.
“Fabulyst makes it easier for shoppers to find the perfect purchases by matching inventory items to users’ specific, personalized queries (e.g., suiting their body type or skin tone). At the same time, we help retailers to achieve more effective conversions by using computer vision to forecast monthly trends based on data from social media, search, blogs etc. and auto-tagging those trends within our retail customers’ catalogs. Fabulyst uses AWS to deliver our best-in-class solutions, including Amazon SageMaker to handle the many predictions that support our offerings. Relying on SageMaker and other AWS services, we are able to guarantee value to our users – such as a 10% boost in incremental revenue for retailers – and have confidence in our ability to deliver super results every time.”
Komal Prajapati, Founder & CEO - Fabulyst
Terragon Group is a data and marketing technology business that unlocks value for businesses using insight to reach the mobile audience in Africa. Over the years, Terragon Group has become a leader in the mobile space serving local and multi-national brands, spanning across multiple geographies. Delivering the right ad message to the right user at the right moment requires personalization and Terragon uses data, insights, and Artificial Intelligence to help businesses reach the right audience in Africa.
“Amazon SageMaker provides an end-to-end machine learning workflow for us without the need for any underlying infrastructure plumbing. Our data science and machine learning teams are able to go quickly from data exploration to model training and production in just a couple of hours. for a business based in Africa with scarce engineering talent, there’s no other way we would have been able to build and deploy ML modes solving real life problems in less than 90 days.”
Deji Balogun, CTO - Terragon Group
SmartNews is the largest news app in Japan delivering quality information to more than 11 million monthly active users in the world. With machine learning technologies, SmartNews helps users with the most relevant and interesting news stories. The machine learning algorithms at SmartNews evaluate millions of articles, social signals, and human interactions to deliver the top 0.01% of stories, that matter most, right now.
"Our mission to discover and deliver quality stories to the world is powered by AWS and particularly Amazon SageMaker, which has helped us accelerate the development cycle to serve our customers. Using Amazon SageMaker has helped us immensely in our news curation methods including article classification using deep learning, predicting Life Time Value, and composite modeling for text and image. We look forward to achieving greater heights with Amazon SageMaker and other AI solutions from AWS.”
Kaisei Hamamoto, Co-Founder and Co-CEO - SmartNews, Inc.
Signate offers solutions for outsourcing, hiring, and consulting services, using AI. Signate is also known as a data science community with more than 16,000 members where they compete each other to produce best models in the competitions. The company also offers a service using Amazon SageMaker that helps its clients deploy the models obtained through competitions into production applications.
“We are leveraging Amazon SageMaker as our principal tool to build our machine learning models and this has made our model management system called “Aldebaran” more scalable. SageMaker has enabled seamless integration into our workflows including building, training, and deploying ML models simultaneously. Previously, it used to take us 3 to 6 months to deploy models into production. With SageMaker, we can deploy a model into production in 1 to 4 weeks, saving time and increasing productivity. SageMaker is our standard ML platform of choice for all our ML models”.
Shigeru Saito, President CEO/CDO - Signate Inc.
Pioneer is a multinational corporation that specializes in digital entertainment including car electronics and mobility services. Pioneer is driven by its corporate philosophy of "Move the Heart and Touch the Soul", and provides its customers with products and services that can help them in their everyday lives.
“Leveraging Amazon SageMaker and the model training features such as Automatic Model Tuning, we were able to develop highly accurate machine learning models, and continue to ensure privacy for our customers. We are also looking forward to leveraging AWS Marketplace for Machine Learning for both algorithms and pre-trained models, to build a monetization platform."
Kazuhiro Miyamoto, General Manager Information Service Engineering Department - Pioneer
Dely is running Japan's best cooking video service, Kurashiru. It strives every day to make culinary services that impact the world. Kurashiru helps many people per day, where it introduces a variety of tasty food recipes that color the dining table with cooking videos. Tens of millions of people watch and listen to the monthly recipe service in Japan.
“We exceeded 15 million downloads of our mobile app, in 2.5 years since we launched the popular Kurashiru service. We believe it is critical to deliver the right content to our users at the right time using advanced technologies such as machine learning. To achieve this, we used Amazon SageMaker that helped us build and deploy the machine learning models in production in 90 days. We also improved the Click-Through Rate by 15% with content personalization”.
Masato Otake, CTO - Dely, Inc.
Ayla Networks is a San Francisco-based IoT platform-as-a-service software company that develops solutions for both the consumer and commercial markets.
“At Ayla Networks, we find our customers primarily run on AWS infrastructure due to its proven scalability and reliability. In particular, we see that commercial manufacturers are leveraging Amazon SageMaker to harness the equipment performance data from the Ayla Cloud. With Amazon SageMaker and our Ayla IQ product, businesses can reveal insights and anomalies that lead to better product and service quality, even insofar as predicting machine failures and remedying them before they can occur. This solution keeps our customers running smoothly so their businesses can keep growing, producing, and scaling without worry.”
Prashanth Shetty, VP of Global Marketing - Ayla Networks
FreakOut is a leading technology company focused around digital advertisements. The company offers products for real-time ad inventory transactions in internet advertising as well as data analysis for browsing the web. FreakOut leverages machine learning for click-through rate (CTR) and conversion rate (CVR) predictions.
“We are in the process of migrating machine learning training environments from on-premises to Amazon SageMaker. Amazon SageMaker offers us a more scalable solution for our business. With the Automatic Model Tuning feature from Amazon SageMaker, we can optimize and estimate highly accurate models for our requirements."
Jiro Nishiguchi, CTO - FreakOut