AWS and Scuderia Ferrari HP streamline F1 power unit assembly | Amazon Web Services

Amazon Web Services · Advanced ·☁️ DevOps & Cloud ·1y ago
AWS and Scuderia Ferrari HP's partnered to create an innovative approach to Formula 1 power unit assembly. AWS worked with the team to migrate their manufacturing data to the cloud, including the vast amounts of information generated during the preparation and assembly of individual power units—the lifeblood of any F1 car. Learn how the team used Amazon SageMaker AI to build a processing pipeline that can now process at least four times the amount of data compared to previous manual methods, while reaching insights in half the time. Learn more about Ferrari on AWS - http://go.aws/4k88W2A Subscribe to AWS: https://go.aws/subscribe Sign up for AWS: https://go.aws/signup AWS free tier: https://go.aws/free Explore more: https://go.aws/more Contact AWS: https://go.aws/contact Next steps: Explore on AWS in Analyst Research: https://go.aws/reports Discover, deploy, and manage software that runs on AWS: https://go.aws/marketplace Join the AWS Partner Network: https://go.aws/partners Learn more on how Amazon builds and operates software: https://go.aws/library Do you have technical AWS questions? Ask the community of experts on AWS re:Post: https://go.aws/3lPaoPb Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—use AWS to be more agile, lower costs, and innovate faster. #AWS #SageMaker #MachineLearning #ArtificialIntelligence #AmazonWebServices #CloudComputing

What You'll Learn

AWS and Scuderia Ferrari HP partnered to streamline F1 power unit assembly using AWS services such as Amazon S3, Amazon SageMaker Studio, and AWS Glue to centralize and automate data, improving performance and reliability.

Full Transcript

[Music] [Applause] Power unit management is quite complex because from one side from the design and manufacturing phases on we need to improve the performance but from the other side we need to enhance the reliability of the power unit. As you may know the FAA impose uses the uh a limit in usage of the power unit per driver. So one of the most uh challenging aspect on this work is to try to balance performance and reliability. AWS is helping us in centralizing and automating the data. Previously we had to manual review data from the assembly process. that are spread across different systems. So for the engineers it was quite difficult to spot anomalies or to detect some trends. Uh we built a centralized access point for the data fully serverless and event driven architecture through AWS infrastructure. The the core components of our solution are Amazon S3 where we built a data link uh with power units uh data coming from all on premise data sources. Uh Amazon SageMaker Studio where we built uh and developed a custom model for the anomaly detection. also AWS glue for ETL purposes and also for the calculation of functional drifts over time for engine components. Paradise manufacturing processes drifts refers to gradual unintended changes in critical measurements or performance parameter over time like for example the declining of the power or fuel efficiency. Essentially it indicates when a power unit is start to operate uh outside its normal working working parameters. With the help of AWS our engineers now can access the data in a centralized point through AWS quicksite. Now we can have some dedicated dashboard that are merging all these data. This brings us to a different kind of analysis more accurate, more faster. It means that we can act during the assembly process in order to avoid any kind of deviation from the standard. So what we are thinking now is to expand uh the way we do this analysis. We're going to expand this uh in other areas of the scooter for RHP. Having AWS services that help us in managing automatically this kind of maintenance is really uh an help. [Music]
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Amazon Web Services · Amazon Web Services · 4 of 60

1 Agentic AI Design Patterns Introduction and walkthrough | Amazon Web Services
Agentic AI Design Patterns Introduction and walkthrough | Amazon Web Services
Amazon Web Services
2 Galileo on modernizing on banking infrastructure | Amazon Web Services
Galileo on modernizing on banking infrastructure | Amazon Web Services
Amazon Web Services
3 Alliander Speeds Innovation and Energy Transition Using AWS | Amazon Web Services
Alliander Speeds Innovation and Energy Transition Using AWS | Amazon Web Services
Amazon Web Services
AWS and Scuderia Ferrari HP streamline F1 power unit assembly | Amazon Web Services
AWS and Scuderia Ferrari HP streamline F1 power unit assembly | Amazon Web Services
Amazon Web Services
5 How AWS machine learning supports Scuderia Ferrari HP pit stops | Amazon Web Services
How AWS machine learning supports Scuderia Ferrari HP pit stops | Amazon Web Services
Amazon Web Services
6 Nasdaq Builds Market Infrastructure of the Future with AWS | Amazon Web Services
Nasdaq Builds Market Infrastructure of the Future with AWS | Amazon Web Services
Amazon Web Services
7 AWS Security Hub Exposure Findings | Amazon Web Services
AWS Security Hub Exposure Findings | Amazon Web Services
Amazon Web Services
8 How do I use Session Manager port forwarding to connect to my EC2 instance through RDP?
How do I use Session Manager port forwarding to connect to my EC2 instance through RDP?
Amazon Web Services
9 How do I extend an EBS volume with LVM partitions?
How do I extend an EBS volume with LVM partitions?
Amazon Web Services
10 AWS Graviton makes it easy to optimize performance, cost, and sustainability | Amazon Web Services
AWS Graviton makes it easy to optimize performance, cost, and sustainability | Amazon Web Services
Amazon Web Services
11 Run Cloud Adoption Framework workshops with Miro | Amazon Web Services
Run Cloud Adoption Framework workshops with Miro | Amazon Web Services
Amazon Web Services
12 Getting Started with AWS Cost Optimization Hub | Amazon Web Services
Getting Started with AWS Cost Optimization Hub | Amazon Web Services
Amazon Web Services
13 Why did my Amazon SQS messages get sent to a dead-letter queue?
Why did my Amazon SQS messages get sent to a dead-letter queue?
Amazon Web Services
14 Declarative Policies for EC2 | Amazon Web Services
Declarative Policies for EC2 | Amazon Web Services
Amazon Web Services
15 How do I troubleshoot IAM permission issues for the Billing and Cost Management console?
How do I troubleshoot IAM permission issues for the Billing and Cost Management console?
Amazon Web Services
16 Integrity at Scale: Inside the Flo Health Mission | Amazon Web Services
Integrity at Scale: Inside the Flo Health Mission | Amazon Web Services
Amazon Web Services
17 Fueling Success: Small shifts, powerful performance | Amazon Web Services
Fueling Success: Small shifts, powerful performance | Amazon Web Services
Amazon Web Services
18 WEX enhances customer experience with AI-powered chatbot | Amazon Web Services
WEX enhances customer experience with AI-powered chatbot | Amazon Web Services
Amazon Web Services
19 Accelerate troubleshooting with Amazon CloudWatch investigations | Amazon Web Services
Accelerate troubleshooting with Amazon CloudWatch investigations | Amazon Web Services
Amazon Web Services
20 Why is my Windows WorkSpace stuck in the starting, rebooting, or stopping status?
Why is my Windows WorkSpace stuck in the starting, rebooting, or stopping status?
Amazon Web Services
21 Telemetry Pipelines for AI | Amazon Web Services
Telemetry Pipelines for AI | Amazon Web Services
Amazon Web Services
22 Getting Control over Security and Observability Data | Amazon Web Services
Getting Control over Security and Observability Data | Amazon Web Services
Amazon Web Services
23 The Problem with Telemetry Data Volume | Amazon Web Services
The Problem with Telemetry Data Volume | Amazon Web Services
Amazon Web Services
24 Telemetry Pipelines on AWS | Amazon Web Services
Telemetry Pipelines on AWS | Amazon Web Services
Amazon Web Services
25 What are Telemetry Pipelines? | Amazon Web Services
What are Telemetry Pipelines? | Amazon Web Services
Amazon Web Services
26 Using AI for RegEx on Telemetry Pipelines | Amazon Web Services
Using AI for RegEx on Telemetry Pipelines | Amazon Web Services
Amazon Web Services
27 Multi-Session Support in the AWS Console | Amazon Web Services
Multi-Session Support in the AWS Console | Amazon Web Services
Amazon Web Services
28 How CloudHedge delivers assessment with AWS ISV Tooling Program at no cost?
How CloudHedge delivers assessment with AWS ISV Tooling Program at no cost?
Amazon Web Services
29 How customers speed up migration and modernization to AWS with CloudHedge | Amazon Web Services
How customers speed up migration and modernization to AWS with CloudHedge | Amazon Web Services
Amazon Web Services
30 Chaos Experiment with Amazon ElastiCache | Amazon Web Services
Chaos Experiment with Amazon ElastiCache | Amazon Web Services
Amazon Web Services
31 Amazon S3 Access Points: Easily manage access for shared datasets on S3 | Amazon Web Services
Amazon S3 Access Points: Easily manage access for shared datasets on S3 | Amazon Web Services
Amazon Web Services
32 ElastiCache Valkey 8.0 - Savings and Efficiency | Amazon Web Services
ElastiCache Valkey 8.0 - Savings and Efficiency | Amazon Web Services
Amazon Web Services
33 Pennymac scales document processing with AWS | Amazon Web Services
Pennymac scales document processing with AWS | Amazon Web Services
Amazon Web Services
34 AWS | Next Level Innovation | Amazon Web Services
AWS | Next Level Innovation | Amazon Web Services
Amazon Web Services
35 Driving Cloud Innovation: Mindtickle's Partnership with AWS Enterprise Support | Amazon Web Services
Driving Cloud Innovation: Mindtickle's Partnership with AWS Enterprise Support | Amazon Web Services
Amazon Web Services
36 A Leader's Edge from Executive Insights | Amazon Web Services
A Leader's Edge from Executive Insights | Amazon Web Services
Amazon Web Services
37 How do I create a custom Amazon WorkSpaces image?
How do I create a custom Amazon WorkSpaces image?
Amazon Web Services
38 Charles Leclerc tests his AI-generated race track | Amazon Web Services
Charles Leclerc tests his AI-generated race track | Amazon Web Services
Amazon Web Services
39 Redington Scales India’s Cloud Access with AWS Partnership | Amazon Web Services
Redington Scales India’s Cloud Access with AWS Partnership | Amazon Web Services
Amazon Web Services
40 How do I prevent the resources in my CloudFormation stack from getting deleted or updated?
How do I prevent the resources in my CloudFormation stack from getting deleted or updated?
Amazon Web Services
41 How do I troubleshoot authentication errors when I use RDP to connect to an EC2 Windows instance?
How do I troubleshoot authentication errors when I use RDP to connect to an EC2 Windows instance?
Amazon Web Services
42 Exploring the Possibilities of Digital Twin & AI at the Edge | Amazon Web Services
Exploring the Possibilities of Digital Twin & AI at the Edge | Amazon Web Services
Amazon Web Services
43 Exploring the Possibilities of Digital Twin & AI at the Edge | Amazon Web Services
Exploring the Possibilities of Digital Twin & AI at the Edge | Amazon Web Services
Amazon Web Services
44 AWS at the FORMULA 1 AWS GRAN PREMIO DELL'EMILIA-ROMAGNA 2025 | Amazon Web Services
AWS at the FORMULA 1 AWS GRAN PREMIO DELL'EMILIA-ROMAGNA 2025 | Amazon Web Services
Amazon Web Services
45 What's new in RCPs | Amazon Web Services
What's new in RCPs | Amazon Web Services
Amazon Web Services
46 API Caching using Amazon ElastiCache | Amazon Web Services
API Caching using Amazon ElastiCache | Amazon Web Services
Amazon Web Services
47 Pendula: Amazon Nova Customer Testimonial | Amazon Web Services
Pendula: Amazon Nova Customer Testimonial | Amazon Web Services
Amazon Web Services
48 InDebted : Amazon Nova Customer Testimonial | Amazon Web Services
InDebted : Amazon Nova Customer Testimonial | Amazon Web Services
Amazon Web Services
49 Amazon DynamoDB global tables with multi-Region strong consistency | Amazon Web Services
Amazon DynamoDB global tables with multi-Region strong consistency | Amazon Web Services
Amazon Web Services
50 Siemens Mobility uses AWS to operate securely, efficiently on a global scale | Amazon Web Services
Siemens Mobility uses AWS to operate securely, efficiently on a global scale | Amazon Web Services
Amazon Web Services
51 How do I reuse a knowledge base session in Amazon Bedrock?
How do I reuse a knowledge base session in Amazon Bedrock?
Amazon Web Services
52 EP5: MBZUAI, CMU : Causal AI, Answering The “Why“ and “What if“ Questions | AWS for AI Podcast
EP5: MBZUAI, CMU : Causal AI, Answering The “Why“ and “What if“ Questions | AWS for AI Podcast
Amazon Web Services
53 Hema scales time to market developing a data mesh on AWS (Technical) - Cloud Adventures
Hema scales time to market developing a data mesh on AWS (Technical) - Cloud Adventures
Amazon Web Services
54 Hema scales time to market developing a data mesh on AWS (Business) - Cloud Adventures
Hema scales time to market developing a data mesh on AWS (Business) - Cloud Adventures
Amazon Web Services
55 How Langfuse Scaled Their AI Platform with AWS: From Open-Source to Enterprise | Amazon Web Services
How Langfuse Scaled Their AI Platform with AWS: From Open-Source to Enterprise | Amazon Web Services
Amazon Web Services
56 SLMs and LLMs: What’s the Difference? | Amazon Web Services
SLMs and LLMs: What’s the Difference? | Amazon Web Services
Amazon Web Services
57 SLMs and LLMs: When to use them? | Amazon Web Services
SLMs and LLMs: When to use them? | Amazon Web Services
Amazon Web Services
58 SLMs on CPU | Amazon Web Services
SLMs on CPU | Amazon Web Services
Amazon Web Services
59 Intelligent Model Routing | Amazon Web Services
Intelligent Model Routing | Amazon Web Services
Amazon Web Services
60 SLMs, LLMs, and Model Routing in Agents | Amazon Web Services
SLMs, LLMs, and Model Routing in Agents | Amazon Web Services
Amazon Web Services

AWS and Scuderia Ferrari HP partnered to improve F1 power unit assembly using AWS services, enabling centralized data access and automated anomaly detection. This collaboration enhanced performance and reliability, and can be applied to other areas of the business.

Key Takeaways
  1. Migrate manufacturing data to the cloud using Amazon S3
  2. Build a custom model for anomaly detection using Amazon SageMaker Studio
  3. Use AWS Glue for ETL purposes and calculate functional drifts
  4. Create a centralized access point for data using a serverless and event-driven architecture
  5. Develop dedicated dashboards to merge and analyze data
💡 Centralizing and automating data using cloud services can significantly improve the efficiency and accuracy of manufacturing processes, allowing for faster and more accurate analysis and decision-making.

Related AI Lessons

Up next
Containers on Amazon ECS with Mama J
AWS Developers
Watch →