Nathan Arnold
Employers on the program
- 2024–2026Amazon Web Services3 talks
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- AWS re:Invent
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Nov 30, 2026 | AWS re:Invent 2026 | AWS re:Invent | chalk talk | Build a Physical AI Model Factory on SageMaker HyperPod as Amazon Web Services Physical AI teams in robotics and autonomous vehicles can't collect enough real data for the rare edge cases that dominate risk. The answer is a continuous data…Show lessPhysical AI teams in robotics and autonomous vehicles can't collect enough real data for the rare edge cases that dominate risk. The answer is a continuous data flywheel: generate synthetic scenarios, post-train policies, evaluate, repeat. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | workshop | Cost-efficient AI model scaling with Amazon EKS on SageMaker HyperPod as Amazon Web Services Amazon SageMaker HyperPod accelerates AI model development by eliminating the undifferentiated heavy lifting involved in building and maintaining compute…Show lessAmazon SageMaker HyperPod accelerates AI model development by eliminating the undifferentiated heavy lifting involved in building and maintaining compute clusters powered by thousands of accelerators. Amazon Elastic Kubernetes Service (EKS) provides enhanced scalability and resiliency tooling to scale AI/ML workloads. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | chalk talk | Cost-effectively deploy PyTorch LLMs on AWS Inferentia using EKS as Amazon Web Services Unlock the full potential of large language models (LLMs) on Amazon EKS by optimizing inference performance and cost efficiency. This chalk talk provides…Show lessUnlock the full potential of large language models (LLMs) on Amazon EKS by optimizing inference performance and cost efficiency. This chalk talk provides practical guidance on deploying and scaling PyTorch-based LLMs on EKS using AWS Inferentia, Karpenter, and KServe. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Keita Watanabe | 3 | 1 | 2026 |
| Aaron Chen | 3 | — | 2026 |
| Aaron Sempf | 3 | — | 2026 |
| Aarthi Raju | 3 | — | 2026 |
| Abbas Mooraj | 3 | — | 2026 |
| Abhijeet Jangam | 3 | — | 2026 |
| Abhishek Pandey | 3 | — | 2026 |
| Abhishek Sharma | 3 | — | 2026 |
| Abhishek Srivastav | 3 | — | 2026 |
| Abilashkumar P C | 3 | — | 2026 |
| Adam Hill | 3 | — | 2026 |
| Adam Richter | 3 | — | 2026 |
| Addy Upreti | 3 | — | 2026 |
| Aditi Garg | 3 | — | 2026 |
| Aditya Challa | 3 | — | 2026 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.