Oct 09, 2026

High-Performance Engineering: Forward Deployed at Aston Martin F1

A session on CoreWeave's forward deployed engineering partnership with the Aston Martin Aramco F1 Team, covering why Formula 1 is an unforgiving environment for AI, and three projects: a unified data platform for aerodynamic and telemetry data, Delta to Map, a small transformer model that reduces the gap between wind tunnel predictions and track measurements, and Radio Insights, a fine-tuned speech-to-text and agentic system that makes sense of 40 live radio channels. The discussion extracts lessons applicable to any team moving AI from experiment to production under fixed deadlines.

Sep 30, 2026

AslanX Flow in Action: Training & Deploying a CFD Surrogate Model | Deep Dive — Mason Seo

Mason Seo, CTO of Narnia Labs, demonstrates AslanX Flow, a platform enabling engineers to train, evaluate, and deploy surrogate models for 3D field prediction (e.g., CFD aerodynamics) without a dedicated machine learning engineer. The deep dive covers building a training workflow from raw engineering data, comparing model architectures like PointNet, Transolver, and Linear Neural Operator, and deploying the winning model as a no-code inference application. A key insight shared is the importance of evaluating models on specific engineering data, as public leaderboard performance doesn't always translate, alongside a preview of AI-driven workflow configuration.