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Formula One Group is moving most of its infrastructure from on-premises data centers to AWS and standardizing on AWS machine-learning services—including Amazon SageMaker.
Optimizing Racing with Machine Learning
Using historical race data collected from cars over the past 65 years, Formula 1 data scientists are training deep-learning models that make race predictions and help teams optimize mid-race decisions. The models can predict when teams should pit their cars, determine the best timing for changing tires, and evaluate how drivers are performing.
Bringing Fans onto the Track
Formula 1 then uses AWS data streaming, analytics, and media services to deliver insights about driver decisions and car performance to its more than 500 million fans.
Building for the Future
Because Formula 1 runs its high-performance computing workloads in a scalable environment on AWS, the organization can innovate on the Formula 1 racing experience, car design, and more without worrying about capacity.
A Mission to Improve Lives
*510(k) pending at US FDA. Not available for sales in the United States.
Better Care Using Machine Learning
GE Healthcare uses AWS and Amazon SageMaker to ingest data, store data compliantly, orchestrate curation work across teams, and build machine-learning algorithms.
Better, Faster Models
GE Healthcare reduced the time to train its machine-learning models from days to hours, allowing it to deploy models more quickly and continually improve patient care.
iRobot Roomba 900 Completes Cleaning
The Roomba 900 series completes a cleaning mission in the home and returns to the dock for charging.
iRobot processes the home map, calculates the total floor space cleaned and the status code for the cleaning mission, and publishes the metadata to AWS IoT.
Data Streams Available
iRobot uses an AWS IoT rule to put the message into an Amazon Kinesis stream. From Kinesis, iRobot can process the cleaning mission data. Kinesis allows multiple teams to receive the stream of data.
Data Storage and Analysis
AWS Lambda receives the cleaning mission metadata and parses the format to Amazon DynamoDB. Amazon Kinesis batches the mission data and stores it in Amazon S3. Amazon S3 is used as the iRobot data lake for analytics, where all message data is compressed and stored. Once the data is in Amazon S3, iRobot uses the AWS Analytics toolset. Amazon Athena allows iRobot to explore and discover patterns in the data without having to run compute resources all the time.
The cleaning mission is stored in Amazon DynamoDB and linked to a specific robot and consumer.
The consumer gets an alert that informs them of a successful Roomba 900 series cleaning mission.
Epic Games Uses AWS to Power Worldwide Game Fortnite
Learn how Epic Games uses AWS to deliver Fortnite to more than 200 million players around the world.
Building a Foundation on the Cloud
Epic Games has been using AWS since 2012 and is now all in on the AWS Cloud, running its worldwide game-server fleet, backend platform systems, databases, websites, analytics pipeline, and processing systems on AWS.
An Overnight Sensation
In 2017, Epic Games launched Fortnite, a cross-platform, multiplayer game that became an overnight sensation. In its first year, Fortnite’s user base grew by more than 100 times to 200 million players worldwide.
Pushing the Boundaries of Scale
AWS is integral to the success of Fortnite. Using AWS, Epic Games hosts in-game events with hundreds of millions of invited users without worrying about capacity, ingests 125 million events per minute into its analytics pipeline, and handles data-warehouse growth of more than 5 PB per month.
Providing the Best Gaming Experience
Using AWS, Epic Games is always improving the experience of its players and offering new, exciting games and game elements. The company plans to expand its use of AWS services in the future, including machine learning and containerized services.
Real-Time Container Tracking
Matson built a flagship mobile application for global container tracking that allows customers to perform real-time tracking of their freight shipments. Other valuable features in the application include interactive vessel schedule searching, location-based port map lookups, and live gate-camera feeds.
Mobile Device Access
All mobile devices access AWS via Amazon API Gateway. This provides highly available edge located endpoints for access into resources within Matson's existing virtual private clouds.
The AWS Lambda functions are designed using the microservices pattern and are modeled around specific ocean-based business contexts, such as shipment tracking and vessel schedules.
Database Configuration and Storage
Amazon DynamoDB manages configuration as well as user-feedback configuration and user-feedback notifications sent from mobile devices. DynamoDB Streams provides real-time notifications to Matson's customer service team.
Data Monitoring and Alerts
End-to-End Serverless Application
Matson can now offer customers an end-to-end serverless application to help track their shipments, and has no infrastructure to maintain.
BP Improves Effectiveness and Gains Cost Agility and Speed for Its Critical Business AppsLearn More
Managing Critical Business Apps
BP's IT organization manages SAP applications used by thousands of employees worldwide for supply chain, procurement, finance, and more.
Improving Speed & Cost Agility
To improve speed and gain cost agility, BP used Amazon EC2 to migrate these core business apps to the cloud. In addition, the team built EC2 X1 instances to increase scale and to power their real-time analytics.
The team can now stand up systems on demand in hours instead of weeks or months. BP is seeing performance increases across the board, including a 40 percent speed improvement for the Lubricants ERP system.