Aman Shanbhag
Employers on the program
- 2024–2025Amazon Web Services4 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 | chalk talk | Advanced distributed training strategies for optimal GPU utilization as Amazon Web Services In this session, we will dive deep into advanced strategies for distributed training, including parallelism and communications collectives, storage, and…Show lessIn this session, we will dive deep into advanced strategies for distributed training, including parallelism and communications collectives, storage, and networking. | |
| 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 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | Distributed inference on AWS: Deep dive into inference optimizations as Amazon Web Services As Large Language Models (LLMs) become integral to applications, optimizing inference performance is paramount, especially at scale. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | builders session | Accelerate FM pre-training on Amazon SageMaker HyperPod [REPEAT] as Amazon Web Services Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training…Show lessAmazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs), reducing training time by up to 40%. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Keita Watanabe | 2 | 3 | 2025 |
| Nathan Arnold | 2 | 1 | 2025 |
| Pooja Karadgi | 2 | 1 | 2025 |
| Aaron Chen | 2 | — | 2025 |
| Aaron Dailey | 2 | — | 2025 |
| Aaron Sempf | 2 | — | 2025 |
| Aarthi Raju | 2 | — | 2025 |
| Abbas Mooraj | 2 | — | 2025 |
| Abhi Khanna | 2 | — | 2025 |
| Abhi Shivaditya | 2 | — | 2025 |
| Abhijeet Jangam | 2 | — | 2025 |
| Abhilash Nagilla | 2 | — | 2025 |
| Abhishek Pandey | 2 | — | 2025 |
| Abhishek Sharma | 2 | — | 2025 |
| Abhishek Srivastav | 2 | — | 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.