GE Healthcare uses Amazon SageMaker to build machine-learning models that help healthcare providers identify health issues faced by people around the world. The company runs its GE Health Cloud—which connects to 500,000 imaging devices—on AWS and uses Amazon SageMaker to ingest de-identified data, store that data compliantly, orchestrate work across teams, and build deep-learning algorithms to identify critical health conditions.
Peloton relies on AWS to power its on-demand, live leaderboard. Learn More>>
Cerner chose AWS to power its machine learning and artificial intelligence. Learn More>>
Expedia is all in on AWS, with plans to migrate 80 percent of its mission-critical apps. Learn more »
Atlassian uses AWS to scale its issue-tracking software applications and enhance its disaster recovery and availability. Learn more »
Discover what customers are doing with AWS today
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Al Tayer Group Case Study
Al Tayer Group relies on AWS to scale to meet 50 percent web traffic growth, reduce operational costs by 79 percent, and focus on development instead of infrastructure management. The organization is a privately held holding company with operations in six countries in the Middle East. Al Tayer Group runs four e-commerce websites and a website platform on AWS, taking advantage of Amazon EC2 Spot Instances to reduce costs.
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Skyscanner Case Study - AWS Support
AWS Support helps Skyscanner to ensure it has the right balance for its for its large volume of EC2 instances (reserved, on-demand, and spot) to make sure its business is optimized for both performance and cost.
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Third Rock Ventures Case Study
Machine learning is increasingly used to support therapeutic discovery at biotech companies. Third Rock Ventures is a venture firm that builds and launches biotech companies that accelerate scientific discoveries and transform patient lives. Dozens of Third Rock portfolio companies make use of AWS services to support their infrastructure, data science, bioinformatics, and machine-learning initiatives. Amazon Elastic Compute Cloud (Amazon EC2) along with flexible storage modalities such as Amazon Elastic File System (Amazon EFS) and Amazon Elastic Block Store (Amazon EBS) empower teams to aggregate complex, semi-structured datasets. Amazon SageMaker notebooks provide a cloud solution for reproducible, easy to implement data science workflows. Similarly, Amazon Comprehend Medical makes it easy to structure medical text data, which enables biotech companies to better collaborate with clinical partners and identify opportunities to serve patients
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Apixio Case Study
Apixio uses machine learning to analyze structured and unstructured health data sets to model the quality of patient care. Apixio is a data science company for healthcare that extracts and analyzes data from health records to generate longitudinal insights into patient health. Apixio’s machine learning platform was used to analyze over 4.5 million health charts in 2018, and reduces the time and effort to code health charts by 80%. The company builds its cloud infrastructure and machine learning platform using Amazon Elastic Compute Cloud (Amazon EC2) and relies on Amazon EC2 Spot Instances to take advantage of unused cloud capacity and control costs. The company also stores 2 petabytes of data on Amazon Simple Storage Service (Amazon S3).
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AWS Case Study: AbiBird
With AWS, AbiBird delivers a global solution that will help elderly people live independently at home. AbiBird provides a service that uses smart sensors that monitor lifestyle routines of elderly people at home without intruding on their personal privacy. The backend for the service uses AWS Lambda and AWS IoT Core to ingest, process, and distribute data from the sensors.
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HappyFresh Case Study
Startup HappyFresh meets traffic peaks—when sales double on average after successful campaign launches—and cost-effectively introduces ML to support personalization with AWS. HappyFresh partners with supermarkets and food stores that use the HappyFresh platform to offer online grocery shopping. The company uses services such as AWS Lambda to support fraud detection and Amazon SageMaker for ML-based personalization.
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Joongonara Case Study
Joonggonara improved time-to-market by 4x and enhanced its customer experience. Joonggonara is Korea’s largest online used goods commerce platform. It runs Amazon RDS on Amazon EC2 to improve availability and uses Amazon Elastic MapReduce with Amazon Elasticsearch Service to improve searches.
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Deputy Case Study
Launching its software on the AWS Cloud has given Deputy the compute power, innovative managed services, and global footprint needed to expand its business to 100,000 customers in 10 years. Deputy is an independent software vendor based in Sydney, Australia, offering an automated-scheduling solution to streamline workforce management. The company uses AWS Lambda for serverless computing, Amazon Rekognition for facial analysis, and Amazon Redshift as a data warehouse.
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Spring Venture Group Case Study
Using AWS Services, Spring Venture Group meets the growth demands required of the sector it serves. Spring Venture Group is the largest U.S. direct-to-consumer brokerage of Medicare insurance services. The company uses AWS Service Catalog to ensure fast server provisioning that complies with Amazon HIPAA BAA.
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