Even in auto racing, it’s all about the data today. Amazon Web Services Inc. is a global partner of Formula One and has been the official cloud and machine learning provider for the league since 2018. The dynamic, longstanding partnership comes down to three core pillars: transforming data into racing intelligence, fan experience enhancement and technical transformation.

These three pillars are built on a foundation of data. F1 is, by far, the most intensive sports environment in the world. Each car is outfitted with more than 300 sensors and generates more than 1 million data points per second during a race. The 10 race teams will receive all the data for their two cars only and Formula One can see a narrow band of data across all 20 cars.

F1 analytics built on a foundation of data

Simultaneously, environmental data is being collected as well. More than 20 trackside weather stations continuously monitor temperature, humidity, wind speed and direction. Tires degradation is monitored through surface temperature sensors, pressure monitoring systems and track telemetry. Additionally, high-precision GPS tracks cars within millimeters to provide real-time position updates, speed through corners, racing line analysis and overtaking probability.

During a typical race weekend, AWS processes more than 5 billion data points across all the cars and other systems. This equates to about 500 terabytes of data, which is transmitted over dual 10-gigabit-per-second fiber lines from the Event Technical Center at the track to the Media & Technical Center at Biggin Hill in the south of England. From that location, live video and data is sent over two dedicated AWS Direct Connect links into the AWS cloud.

It’s this massive scale of data that enables AWS to deliver insights and innovation to F1, transforming how teams compete and enabling fans to experience the sport in different ways. One might think given the criticality of F1 data it might be faster and lower cost to stand up a private cloud. At the recent F1 US Grand Prix event in Austin, I met with Ceileidh Siegel, global head of digital innovation for AWS Industries, and she explained that the latency is mere milliseconds and leveraging the cloud removes much of the complexity of continually trying to stand up and tear down private clouds at each race.

Machine learning models are trained before every race and season on Amazon SageMaker and stored in an S3 bucket. Live streaming data is ingested via the Amazon API Gateways and orchestrated using AWS Lambda storing the results in Amazon DynamoDB, writing logs to Amazon Cloud Watch, and sending the results back to the API Gateway. Having processed the results on Amazon Graviton, all this data is then sent back to F1’s Media & Technology Centre and made available to the race teams and worldwide broadcast in under a second.

F1 Insights turn data into actions

For the race teams, AWS and F1 developed a series of data points called “F1 Insights” that can be used to plan race strategy but then also adapt during the race, which is why the millisecond latency matters. F1 Insights was launched in 2018 with three data points but now has 20. Siegel described F1 Insights as a “portfolio of digital products that are built on data to serve different needs.”

As an example, Close to the Wall can measure how close a driver comes to a wall on a turn within a millimeter of accuracy. Pit Strategy Battle can help teams understand if they may get passed on a pit stop. Others include Car Analysis, Braking Performance, Exit Speed and Projected Knockout Time. Some of these data points are used to enhance the broadcast, while others are only race team- or league-facing.

Track Pulse improves storytelling for the broadcast

For the broadcasters, AWS developed “Track Pulse,” which offers data to enhance storytelling and support commentators. “Track Pulse literally and figuratively uses data to look around corners and build a story that helps commentators do their jobs better with higher accuracy,” Siegel explained. “It also provides options for stories they think will be unfolding as the race goes on. It also quickly creates the graphics that will pop up on screen to tell the story.”

Track Pulse represents a fundamental shift in how the story of Formula 1 is told by turning complex data into interesting narrative that resonate with existing but also new fans. The results bear that out as F1 has seen a 47% increase in viewer understanding with real-time engagement across 200-plus markets.

Insights are also for the IT pro

So much of the focus on AI in sports has been for the fan and teams but AWS is also addressing the needs of the information technology pro. F1 has implemented a generative AI based solution using Amazon Bedrock, Agents and Knowledge Bases where IT staff can submit queries through a chat interface to an AI powered virtual assistant. The AI assistant provides instant, relevant responses, speeding up the decision-making process. For particularly complex issues, there’s still the option to escalate to human experts.

The impact has been dramatic resulting in an 86% reduction in issue resolution time. This means faster, more informed decision-making for race control, and more consistent application of technical regulations. F1 is exploring ways to expand this technology to other areas of operations, enhance fan engagement with AI-driven insights, and further improve race strategy and performance.

F1 cars redesigned in the AWS cloud for more competitive racing

Every couple of years F1 releases new standards to improve the race cars with a goal of increasing competitiveness. In 2022 F1 issued new standards to better understand how the cars interact with each other from modeling computational fluid dynamics, which can be thought of as a virtualized wind tunnel. Historically, F1 would build a physical wind tunnel that could house one car.

Using AI and the cloud, a digital twin was built that could simulate two cars and understand the aerodynamic wake of the trailing car. With the old cars, the wake would push straight back, resulting in a loss of downforce while racing closely. The new design pushes the wake up and lets drivers get even closer, leading to more passing opportunities.

The result is 30% more overtakes since 2022. The car will be going through another redesign in 2026 so we shall see what that brings.

F1 car redesign built on Graviton silicon

One of the critical components of compute is the processor it runs on. Amazon offers a wide range of processors, including its own Graviton processors, which results in up to a 40% savings over other chips. The previously mentioned redesign was moved from an on-premises workload to the AWS cloud running on Graviton.

At the track, I spoke to Ali Saidi, vice president and distinguished engineer for AWS, about this. “With on prem, F1 could only run one simulation every three days,” he said. “By moving to AWS, the increase in compute capacity combined with simulation enabled them to run several simulations per day, significantly speeding up development time. The ability to iterate several times per day advanced knowledge so much faster than could be done before.”

IT and business leaders should use F1’s journey as a lesson learned

Though the F1 and AWS partnership will draw a lot of eyeballs because of the immense popularity of racing today, the lessons learned here are applicable to all businesses. We are entering the AI era where data becomes an organizations most valuable asset. Siegel highlighted this when she said, “The caliber and velocity of your data foundation equals the caliber and velocity of your products,” meaning you’re only as good as your data allows you to be.

Not every organization will generate F1-level volumes of data, but every company is generating more and more every year. It’s critical to understand what data the business has, be able to bring it together and then use AI to find those critical insights.

Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.

Photo: Amazon