Formula 1 will use AWS and Machine Learning to Build Race Strategies and Design Cars
Overview
- Formula 1 has announced it’ll be shifting from on premise data centers to Amazon Web Services
- F1’s data scientists are already using Amazon Sagemaker to train deep learning models on more than 65 years of race data!
- This move is expected to enhance the competition by fine tuning racing strategies, improving data tracking sensors and much more
Introduction
Formula 1 is very much a numbers game. As an enraptured audience watches each race wondering which driver and car will outperform the rest, there’s a huge team of engineers and data scientists behind the scenes using data to maximise the car’s advantage. Given how close these races are, and how much is at stake, making the most of what you have becomes critical.
So it comes as no surprise that Formula 1 recently announced it’ll be switching the vast majority of its infrastructure from on-premise data centers to Amazon’s premier ML offering, Amazon Web Services (AWS). It’s a huge victory for AWS as it’ll help the racing conglomerates in building race strategies, data tracking systems and of course, increasing fan engagement.
Formula 1’s data scientists are already hard at work – they’re using Amazon’s Sagemaker (part of the AWS toolkit) to train deep learning models on more than 65 years of race data! Where is this data stored? Amazon DynamoDB and Amazon Glacier, of course. To know more about Sagemaker and how it works, read our article here.
When you watch a race on your television, you see a bunch of statistics thrown on the screen every few minutes. These numbers will get even more in depth as the data scientists will use various algorithms and techniques to give fans insights into what the team is planning and where you can expect each driver to finish. As an example, these data scientists typically use ML to understand when a driver should come in for a pit stop so that he doesn’t lose any ground to his competition. Other variables each team uses are brake wear, tyre pressure and condition, driver’s health stats, etc. It’s a treasure trove of data.
Another huge advantage of moving to AWS is the power of Amazon Kinesis. The plan is to stream real-time data to AWS using Kinesis which will help Formula 1 teams understand the above variables at a very granular level. It’s a game changer in more ways than one.
You can read more about this announcement on Formula 1’s official blog post here.
Our take on this
Cars and machine learning? Sounds like a match made in heaven! This one is a way for the fans of both Formula 1 as well as sports analytics. A lot of data scientists are familiar with how AWS works so this is guaranteed to increase engagement with them as well. You can expect to see a lot more advanced stats on your screen in the coming races this year.
Machine learning will also play a big part in the aerodynamics that go into designing cars. Quite fascinating, isn’t it?
Subscribe to AVBytes here to get regular data science, machine learning and AI updates in your inbox!
Senior Editor at Analytics Vidhya. Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.
