# High-Performance Engineering: Forward Deployed at Aston Martin F1

## Executive summary

This session details CoreWeave's forward-deployed engineering work with the Aston Martin F1 team, demonstrating how advanced AI and data platforms are used to gain competitive advantage in a high-stakes, data-intensive environment. The team addressed three core areas: unifying diverse data sources (Data Platform), improving aerodynamic predictions by minimizing the error between wind tunnel simulations and real-world track data (Delta-to-Map), and processing massive volumes of noisy, multi-channel radio communication (Radio Insights). The overarching theme is applying AI to move from experimental models to reliable, mission-critical production systems under extreme deadlines.

## Key takeaways

- High-Stakes, Zero-Tolerance Environment: F1 is characterized by 24 races per season, meaning every single race counts towards points, making the learning curve and execution requirements significantly higher than typical corporate projects. This necessitates flawless, real-time operations across a 72-hour race weekend window.
- The Value of Forward Deployment: To understand the true pain points, the engineering team spent time not just in the office, but also at the track and in mission control, realizing that the most critical insights often come from observing operations during live race weekends.
- Data Unification is Foundational: The complexity of F1 data—ranging from slow aerodynamic simulations (hundreds of GB per run) to live, low-latency telemetry from over 100 sensors—requires a unified data platform to ensure all teams are 'singing from the same sheet.'

## Technical details

- Data Platform Architecture: The platform unifies diverse data sources, including wind tunnel simulations, live car telemetry (e.g., pressure on different points of the car), and post-race data. Key features include role-based access control, preventing duplicate data uploads, and enabling the replaying of race data in a simulator for analysis.
- Delta-to-Map (Aerodynamics): This project aims to reduce the error (the 'delta') between predictions made by the wind tunnel and actual track-measured data (downforce and drag). The team found that a small Transformer architecture, trained on a relatively small number of races, was highly effective for this regression task, surpassing initial baseline statistical models.
- Radio Insights Platform: The platform addresses the 'ocean of noise' created by 40 continuous radio channels (internal and competitor). It captures and transcribes all streams, automatically categorizes messages (e.g., for tires), and provides a searchable chat interface, allowing engineers to quickly retrieve critical information during a race or for post-race debriefs.

## Practical implications

- The necessity of building robust, highly available AI systems that function flawlessly under extreme, non-reversible pressure (e.g., race day decisions).
- The value of specialized ML models (like fine-tuned ASR/A2T) that are trained on highly noisy, domain-specific data rather than general datasets.
- Forward-deployed engineering teams must immerse themselves in the operational environment to identify true pain points and unstated requirements.

## Topics

Artificial Intelligence (AI), Data Engineering, Machine Learning (ML), Natural Language Processing (NLP), Aerodynamics, High-Performance Computing, CoreWeave, NetApp, Cohere, Cognition, Zscaler, Weights & Biases

Source: https://www.youtube.com/watch?v=1hZwxy0ZVhA