F1 Driving AWS analytics: What are they and how do they work

To get started, F1 uses AWS CloudFormation templates to deploy its own cloud-based infrastructure. These templates are designed to be easy to use and modify so that F1 can build out its infrastructure without having to worry about any technical details. Once deployed, the infrastructure automatically scales up or down depending on demand.

The next step is to create an environment where F1 can run its algorithms. Using AWS Machine Learning (Amazon ML), F1 creates a scalable model training platform. Amazon ML allows F1 to train their ML models using large datasets, which helps them to learn complex patterns and trends in real-time.

After deploying their ML models, F1 uses the Amazon Elastic Compute Cloud (EC2) to process their massive amounts of data. EC2 offers power to compute at a low cost, making it ideal for running large numbers of jobs simultaneously. For example, F1 could have hundreds of thousands of sensors collecting information from each car during a race, while still being able to process all this data in real-time.

With these tools in place, F1 is now ready to start analyzing its data. To do this, they use Amazon SageMaker, which lets them quickly develop and test new solutions to problems. Amazon SageMaker includes deep learning libraries, allowing F1 to quickly build custom AI applications.

With Amazon SageMaker, F1 can apply their knowledge to specific situations by creating “predictive maintenance” systems that help them predict when components will fail. For example, if a tire fails, F1 can use predictive maintenance to determine whether the failure was caused by a defect in the tire itself or due to a manufacturing defect. If the latter is true, then F1 can order replacement parts before a problem occurs. This reduces downtime and increases safety.

Finally, F1 can use Amazon SageMaker to perform anomaly detection. Anomaly detection looks for unusual events within data sets, such as abnormal behaviors in vehicles or unusual weather conditions. It can identify anomalies even if they occur infrequently, and thus provide early warnings of potential issues.