Sharlina Keshava
Employers on the program
- 2019–2026Amazon 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
- Customer paths to production grade domain-specific AI with Forge breakout 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 | breakout | Customer paths to production grade domain-specific AI with Forge as Amazon Web Services Domain-specific AI delivers accuracy that is difficult for general models to match. In this session, see how real customers use Amazon Nova Forge to accelerate…Show lessDomain-specific AI delivers accuracy that is difficult for general models to match. In this session, see how real customers use Amazon Nova Forge to accelerate the path from idea to production.. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | Automating LLM fine-tuning with multi-agentic orchestration [REPEAT] as Amazon Web Services Domain-specific LLM fine-tuning traditionally demands extensive manual effort. This session explores an innovative multi-agent system built on AWS that…Show lessDomain-specific LLM fine-tuning traditionally demands extensive manual effort. This session explores an innovative multi-agent system built on AWS that continuously improves model performance with minimal human intervention. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | breakout | Fine-tuning LLMs for Multi-Agent Orchestration: Cosine AI Case Study as Amazon Web Services Multi-agent AI systems are becoming essential for multi-step production applications, but require specialized models that are small, fast, and cost-effective.…Show lessMulti-agent AI systems are becoming essential for multi-step production applications, but require specialized models that are small, fast, and cost-effective. This session explores fine-tuning, knowledge distillation, and preference alignment techniques to reduce latency and costs while maintaining performance. | Video |
| Dec 2, 2019 | AWS re:Invent 2019 | AWS re:Invent | chalk talk | Aiding natural-disaster responses with Amazon SageMaker and Amazon Comprehend as Amazon Web Services The first hour after a natural disaster is often referred to as the “golden hour," when responders have the highest chance of saving the lives of those effected…Show lessThe first hour after a natural disaster is often referred to as the “golden hour," when responders have the highest chance of saving the lives of those effected by the disaster—but it's also when they have least amount of information available to them. In this chalk talk, we walk through different ways machine learning can help accelerate a responder’s understanding of the areas impacted and... | Slides |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Ali Saidi | 3 | — | 2026 |
| Amit Kumar | 3 | — | 2026 |
| Andrew Baird | 3 | — | 2026 |
| Andrew Crudge | 3 | — | 2026 |
| Andrew Krug | 3 | — | 2026 |
| Andy Reedy | 3 | — | 2026 |
| Andy Warfield | 3 | — | 2026 |
| Anuj Dewangan | 3 | — | 2026 |
| Arthur Petitpierre | 3 | — | 2026 |
| Artur Rodrigues | 3 | — | 2026 |
| Avni Rambhia | 3 | — | 2026 |
| Brian Beach | 3 | — | 2026 |
| Carlos Manzanedo Rueda | 3 | — | 2026 |
| Chad Schmutzer | 3 | — | 2026 |
| Christian Elsen | 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.