Thursday, February 15, 2024, 10:00 a.m. CET
Learn practical methods for designing and implementing LLM-powered systems on real-world business data using popular, ready-to-go LLM APIs—no specialized hardware, model training, or tricky deployment required. We'll show techniques for engineering effective inputs to the models (“prompts”) and how to combine LLMs with other systems, including business databases, with toolkits workflow frameworks like LangChain. Join us and learn how to build LLM systems to generate tangible business results..
David Taubenheim is a Senior Solutions Engineer in NVIDIA's Developer Programs organization, driving AI approaches to business challenges. Prior to his work at NVIDIA, David directed a program area of engineers, scientists, and technical managers supporting US Government sponsors through specialized technology programs and emergent initiatives at the Johns Hopkins University Applied Physics Laboratory (APL). Earlier, David's work at Motorola focused on the design of bespoke digital signal processing systems using custom software defined radio platforms, earning him the company's Distinguished Innovator Award. In 2018, David received the Engineer of the Year Award by the National Organization of Gay and Lesbian Scientists and Technical Professionals for his contributions to national security technologies. He has a Master of Science in Electrical Engineering and has been granted 17 patents for a variety of technical inventions.
Thursday, February 15, 2024, 10:50 a.m. CET
Push LLMs beyond the quality limits of off-the-shelf models and APIs by customizing them for domain-specific applications. We'll discuss strategies for preparing datasets and showcase gains from different forms of customization using practical, real-world examples. Join us and learn about model tuning techniques applicable for both API-based and self-managed LLMs.
Chris Pang is a Senior Solutions Engineer in NVIDIA's Developer Programs organization. Before joining NVIDIA, Chris worked in baseball analytics. He was a member of the front offices of the New York Mets and New York Yankees, where he split time between working on statistical models for player evaluation and full-stack development of internal web applications. Chris studied Applied Physics at Yale University.
NOT IN USE
Thursday, February 15, 2024, 11:50 a.m. CET
Optimizing and deploying LLMs on self-managed hardware—whether in the cloud or on premises–can produce tangible efficiency, data governance, and cost improvements for organizations operating at scale. We'll discuss open, commercially licensed LLMs that run on commonly available hardware and show how to use optimizers to get both lower-latency and higher-throughput inference to reduce compute needs. Join us and learn how to scale up self-managed LLMs to accommodate unique business and application requirements.
Emily Apsey is a Manager of NVIDIA Technical Marketing Engineers. She works closely with both internal and external stakeholders, on initiative such as marketing, engineering, and business development. Emily is just as happy talking to and teaching others, as she is heads down leading and using NVIDIA AI-Ready Enterprise technolog
NOT IN USE
Thursday, February 15, 2024, 12:20 p.m. CET
In this session, we'll answer any additional questions that attendees may have, beyond those discussed during the sessions.
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Date & Time: Wednesday, April 22, 2018