Daniel Zilberman
Employers on the program
- 2024–2025Amazon 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 |
|---|---|---|---|---|---|
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | builders session | Next-gen AI: Scaling distributed GPU inference with NVIDIA on AWS as Amazon Discover how to achieve massive inference scaling through GPU disaggregation on AWS. Learn architectural patterns for splitting model inference across multiple…Show lessDiscover how to achieve massive inference scaling through GPU disaggregation on AWS. Learn architectural patterns for splitting model inference across multiple NVIDIA GPUs, delivering significantly higher throughput for AI workloads. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | builders session | How to manage and prepare for large-scale and high-traffic events [REPEAT] as Amazon Learn how to provide a smooth customer journey during peak times by implementing a scalable, cloud-based virtual waiting room that leverages AWS services to…Show lessLearn how to provide a smooth customer journey during peak times by implementing a scalable, cloud-based virtual waiting room that leverages AWS services to reduce resource strain. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | chalk talk | Streamline document workflow with complementary gen AI solutions [REPEAT] as Amazon This session demonstrates how Enhanced Document Understanding on AWS and Generative AI Application Builder on AWS can individually or jointly solve document…Show lessThis session demonstrates how Enhanced Document Understanding on AWS and Generative AI Application Builder on AWS can individually or jointly solve document processing and automation use cases. | |
| Nov 27, 2023 | AWS re:Invent 2023 | AWS re:Invent | builders session | PyTorch best practices for generative AI & LLM inference architectures [REPEAT]Generative AI and large language models (LLMs) are rapidly disrupting workflows. Inference architectures for these large-scale models need to be carefully…Show lessGenerative AI and large language models (LLMs) are rapidly disrupting workflows. Inference architectures for these large-scale models need to be carefully constructed to achieve optimal cost performance. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Alex Iankoulski | 3 | 2 | 2025 |
| Keita Watanabe | 3 | 1 | 2025 |
| Aaron Chen | 3 | — | 2025 |
| Aaron Dailey | 3 | — | 2025 |
| Aaron Sempf | 3 | — | 2025 |
| Abbas Mooraj | 3 | — | 2025 |
| Abhishek Pandey | 3 | — | 2025 |
| Abhishek Sharma | 3 | — | 2025 |
| Abhishek Srivastav | 3 | — | 2025 |
| Adam Hill | 3 | — | 2025 |
| Addy Upreti | 3 | — | 2025 |
| Adil Can | 3 | — | 2025 |
| Aditi Garg | 3 | — | 2025 |
| Aditya Challa | 3 | — | 2025 |
| Aditya Kalyanakrishnan | 3 | — | 2025 |
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.