Tomonori Shimomura
Employers on the program
- 2024–2026Amazon Web Services4 talks
Upcoming appearances
Who’s booked ahead? →Event dates shown; only events starting today or later count. 1 upcoming event.
-
AWS re:Invent 2026 – · Las Vegas, United States
- Build and Optimize AI Pipelines with Ray on SageMaker HyperPod builders session Program source ↗
No upcoming appearances are currently recorded.
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 | builders session | Build and Optimize AI Pipelines with Ray on SageMaker HyperPod as Amazon Web Services Ray provides a unified Python framework for petabyte-scale data processing, large-model training, and low-latency inference. In this hands-on workshop, you will…Show lessRay provides a unified Python framework for petabyte-scale data processing, large-model training, and low-latency inference. In this hands-on workshop, you will use Ray on SageMaker HyperPod to a model and set up endpoints. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | builders session | Advanced model customization with Amazon Nova [REPEAT] as Amazon Web Services Master advanced fine-tuning techniques to customize Amazon Nova in this engaging 60-minute hands-on session. Learn to adapt Amazon Nova for financial advisory…Show lessMaster advanced fine-tuning techniques to customize Amazon Nova in this engaging 60-minute hands-on session. Learn to adapt Amazon Nova for financial advisory and investment analysis, including analyzing market data, assessing investment opportunities, and generating personalized financial recommendations. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | breakout | Train high-performing AI models at scale on AWS as Amazon Web Services Training large AI models requires significant compute resources and can be time and cost intensive. In this session, learn to optimize and accelerate your model…Show lessTraining large AI models requires significant compute resources and can be time and cost intensive. In this session, learn to optimize and accelerate your model training workloads using AWS's purpose-built infrastructure and tools. | Video |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | builders session | Fine-tune Llama 3 models cost-effectively on Amazon SageMaker [REPEAT] as Amazon In this builders’ session, get step-by-step implementation experience fine-tuning Llama 3 8B using the QLoRA strategy. Learn how you can fine-tune Llama 3…Show lessIn this builders’ session, get step-by-step implementation experience fine-tuning Llama 3 8B using the QLoRA strategy. Learn how you can fine-tune Llama 3 models at scale using the memory-efficient PyTorch Fully Sharded Data Parallel (FSDP2) library with access to built-in debugging and GPU utilization metrics. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Ioan CATANA | 3 | 1 | 2026 |
| Kanwaljit Khurmi | 3 | 1 | 2026 |
| Roy Allela | 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 |
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.