Businesses worldwide are recognizing the transformative capabilities of Large Language Models (LLMs) in reshaping their operations from customer service to content creation, data analysis and strategic decision-making. However, implementing LLMs within existing workflows comes with its own set of challenges.
To access the full potential of LLMs for enterprise use requires careful consideration and the right approach. On Tuesday, August 22 at 11:00am PST, we were joined by experts from diverse technical backgrounds with extensive experience in AI/ML/NLP to gain unique perspectives and insights on successfully incorporating LLMs into your business.
Panelists shared the key adoption drivers and benefits for these models, approaches to take when implementing LLMs, fine-tuning LLMs on custom datasets for more accurate and relevant outputs, addressing challenges, and more.
Kelsey Hightower is a renowned technology advocate and thought leader in the cloud-native computing space. With extensive experience in Kubernetes, Go, and cloud technologies, Kelsey has made significant contributions to the open-source community. As a frequent conference speaker and author, he shares his expertise, inspiring developers and organizations to embrace innovative solutions and build scalable, reliable systems.
Animesh is leading the next generation AI and ML Platform at LinkedIn enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. He is building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years' experience in Software industry, and 15+ years in AI, Data and Cloud Platform.
Harrison Chase is the CEO and co-founder of LangChain, a company formed around the open source Python/Typescript packages that aim to make it easy to develop Language Model applications. Prior to starting LangChain, he led the ML team at Robust Intelligence (an MLOps company focused on testing and validation of machine learning models), led the entity linking team at Kensho (a fintech startup), and studied stats and CS at Harvard.
Ketan Umare is the CEO and co-founder at Union.ai. Previously he had multiple Senior roles at Lyft, Oracle, and Amazon ranging from Cloud, Distributed storage, Mapping (map-making), and machine-learning systems. He is passionate about building software that makes engineers' lives easier and provides simplified access to large-scale systems. Besides software, he is a proud father, and husband, and enjoys traveling and outdoor activities.
Nalin Dadhich is a core member of developers working on project Tokkio where he works on developing intelligent AI-powered customer service agents. Tokkio amalgamates conversational AI, Vision, LLMs, and Omniverse Animation AI for real-time interactions. He has worked extensively on developing enterprise-scale bots using LLMs with Tokkio which includes both open domain or application-specific question answering through information retrieval.
More details on Tokkio.
Stef is an avid technologist, humanist, and climber living in Berkeley, CA. Stef helped train and put Meta's first LLMs into scaled production for classification tasks in 2018. More recently he has applied them to fraud detection and merchant clustering at Stripe.
Learn how the interplay between modern orchestration techniques and Large Language Models can help businesses achieve maximum value and performance.
