Ramesh Mathikumar
Employers on the program
- 2024–2026Amazon Web Services10 talks
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 | Beyond Batch: Modernize with Event-Driven Architecture & Serverless as Amazon Web Services In this builders' session, transform a batch-oriented data processing pipeline into a real-time, event-driven architecture using Amazon EventBridge, AWS Lambda,…Show lessIn this builders' session, transform a batch-oriented data processing pipeline into a real-time, event-driven architecture using Amazon EventBridge, AWS Lambda, and AWS Step Functions. | |
| Nov 30, 2026 | AWS re:Invent 2026 | AWS re:Invent | chalk talk | Expedite model loading times on Kubernetes using kro and ACK - from storage to GPU as Amazon Web Services LLM inference on Kubernetes is bottlenecked by loading multi-gigabyte model weights through the storage → network → CPU → PCIe → GPU path. | |
| Nov 30, 2026 | AWS re:Invent 2026 | AWS re:Invent | chalk talk | Maximize EKS efficiency: Auto Mode, Graviton, and Spot best practices as Amazon Web Services Achieving optimal compute efficiency on Amazon EKS demands more than choosing the right instance. It requires an intelligent infrastructure layer that…Show lessAchieving optimal compute efficiency on Amazon EKS demands more than choosing the right instance. It requires an intelligent infrastructure layer that continuously optimizes on your behalf. | |
| Nov 30, 2026 | AWS re:Invent 2026 | AWS re:Invent | workshop | Optimize analytics workloads on Amazon EKS for performance and cost as Amazon Web Services The analytics landscape is undergoing a shift as global data volumes grow at unprecedented rates. This rapid growth, combined with rising demand for real-time…Show lessThe analytics landscape is undergoing a shift as global data volumes grow at unprecedented rates. This rapid growth, combined with rising demand for real-time analytics, is driving organizations to modernize and optimize their data processing pipelines and analytics workloads. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | builders session | Beyond Batch: Modernize with Event-Driven Architecture & Serverless [REPEAT] as Amazon Web Services Legacy systems typically employ a batch processing model, where transactions are retrieved from logs or databases at scheduled times and processed. This…Show lessLegacy systems typically employ a batch processing model, where transactions are retrieved from logs or databases at scheduled times and processed. This builders session introduces event-driven patterns as a solution, enabling near real-time processing and removing the constraints of end-of-batch processing. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | Expedite model loading times in Kubernetes using kro & ACK [REPEAT] as Amazon Web Services LLM models cause significant inference latency on EKS/GPUs due to slow loading. The Storage>Network>CPU>PCIe>GPU data path bottlenecks loading latency. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | workshop | Optimize analytics workloads on Amazon EKS for performance and cost as Amazon Web Services The analytics landscape is undergoing a shift as global data volumes grow at unprecedented rates. This rapid growth, combined with rising demand for real-time…Show lessThe analytics landscape is undergoing a shift as global data volumes grow at unprecedented rates. This rapid growth, combined with rising demand for real-time analytics, is driving organizations to modernize and optimize their data processing and analytics workloads. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | Processing payments with real-time serverless architectures as Amazon Web Services Traditional batch processes often introduce significant processing delays and lack the resilience demanded by modern business operations. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | chalk talk | Network packet capture using Mountpoint for Amazon S3 and Karpenter as Amazon WebServices Incident response is crucial for detecting, containing, and recovering from cybersecurity threats. At its core lies data analysis, which requires timely capture…Show lessIncident response is crucial for detecting, containing, and recovering from cybersecurity threats. At its core lies data analysis, which requires timely capture and delivery of relevant data for forensic analysts and incident responders to act upon. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | chalk talk | Process payments in near real time with serverless architectures as Amazon WebServices Customers around the globe expect payments to be made in real time. Learn how to use AWS serverless, event-driven services to unlock real-time payment posting…Show lessCustomers around the globe expect payments to be made in real time. Learn how to use AWS serverless, event-driven services to unlock real-time payment posting capabilities. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Rajdeep Banerjee | 3 | 4 | 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.