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Predictive Analytics
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Financial Services
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Machine Learning
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SERVERLESS
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IoT
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ENTERPRISE APPLICATIONS
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Predictive Analytics
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Formula One Group Uses Amazon SageMaker to Optimize Racing
Learn MoreLearn how Formula 1 uses Amazon SageMaker and other AWS services to optimize racing decisions and improve fan experience.
Beginning a Transformation
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.
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Financial Services
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Intuit Makes Tax Filing Less Taxing Using AWS
Learn how financial technology company Intuit uses Amazon SageMaker to train its machine learning models quickly and at scale, cutting the time needed to deploy models by 90 percent.Learn More
Embracing the Cloud
Intuit is all in on AWS and uses a wide breadth of AWS services to provide the elasticity it needs to handle highly seasonal traffic patterns. Since 2013, Intuit has moved its infrastructure, applications, data, and machine learning capabilities to AWS.
Product Innovation Through Data Science
Intuit explores machine learning as it seeks to make arduous tasks, like filing taxes, easy and even delightful for its customers.
Amazon SageMaker for Machine Learning
Using Amazon SageMaker, Intuit has reduced the cost and the time needed to deploy machine learning models. Data scientists can now create a model and scale it out to many servers, and what used to take six months now takes one week.
Powering Prosperity
By empowering its data scientists, Intuit continues to develop and enhance products to serve its mission: to power prosperity for its customers around the world.
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Machine Learning
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MLB Chooses AWS as Official Machine Learning Provider
Learn How America’s Professional Baseball League Brings Meaning to Statistics Using AWS Machine LearningLearn More
A Foundation for Deep Learning
MLB has been collecting statistical data on its players and clubs for decades, and in 2015 it started using AWS to collect and distribute game-day stats to enhance the fan experience.
Empowering Developers
By using Amazon Sagemaker, MLB is empowering its developers and data scientists to quickly and easily build, train, and deploy machine-learning models at scale.
Lightening the Load
These models help MLB eliminate manual, time-intensive processes associated with recordkeeping and statistics, like scorekeeping, capturing game notes, and classifying pitches.
Personalizing the Game
MLB plans to work with the Amazon ML Solutions Lab to continue improving Statcast—its tracking technology that analyzes player performance—including testing accuracy of pitch predictions and creating personalized viewer experiences.
A Home Run with Artificial Intelligence
MLB will continue to innovate using artificial intelligence. The organization plans to use Amazon Comprehend to build a language model that could create scripts for live games that simulate iconic announcers.
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SERVERLESS
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Matson Operates Its Global Shipping and Logistics Businesses on AWS
Learn how Matson is using AWS to drive innovation and world-class customer service, while achieving operational reliability, security, and infrastructure cost savings.Learn More
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.
Serverless Computing
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
Matson's customers rely on accurate, up-to-the-minute container tracking and vessel status information. Monitoring and alerting of system events is achieved by using Amazon CloudWatch, Amazon SNS, Amazon SES, AWS Lambda, and CloudWatch Logs.
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.
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IoT
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iRobot Delivers on the Promise of Robots in the Smart Home with AWS
Learn how iRobot handles consumer IoT at scale using serverless technology.Learn More
iRobot Roomba 900 Completes Cleaning
The Roomba 900 series completes a cleaning mission in the home and returns to the dock for charging.
Data Processing
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.
Data Correlation
The cleaning mission is stored in Amazon DynamoDB and linked to a specific robot and consumer.
Customer Notification
The consumer gets an alert that informs them of a successful Roomba 900 series cleaning mission.
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ENTERPRISE APPLICATIONS
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BP Improves Effectiveness and Gains Cost Agility and Speed for Its Critical Business Apps
See how BP simplified and modernized its suite of SAP applications, improving user experience while gaining cost agility and enhanced performance.Learn 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.
Increasing Performance
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.
Securing Data
As part of its cloud migration, BP reset its security standards using AWS Config, AWS Identity and Access Management (IAM), Amazon CloudWatch, and AWS Trusted Advisor. These new standards helped BP to develop a secure framework for operating its IT organization.
