Kelsey Hightower

Large Language Models for Enterprise Success: Challenges and Approaches

Large Language Models for Enterprise Success: Challenges and Approaches

To access the full potential of large language models for enterprise use, careful consideration and the right approach are key. LLMs offer many benefits, but their integration into enterprise workflows requires a strategic approach.
In this panel, join experts from diverse technical backgrounds with extensive experience in AI/ML/NLP to gain unique perspectives on successfully implementing LLMs into your business.

Main Discussion Points

  • LLM Adoption and Benefits: Key drivers behind enterprises adopting LLMs and their benefits.
  • Choosing the Right Approach: Comparing the pros and cons of using existing LLMs, prompt engineering, and fine-tuning on custom datasets for different enterprise use cases.
  • Fine-Tuning LLMs: Exploring the advantages and challenges of fine-tuning LLMs on custom datasets to align with specific business objectives.
  • Tools and platforms: Discussing the various tools and platforms to facilitate LLM implementation
  • Overcoming Challenges: Addressing the challenges associated with adopting LLMs, including data privacy, creating high quality datasets, computational resources, ethical considerations, and the need for specialized expertise.
  • Future Directions: Exploring emerging trends, advancements, and potential future applications of LLMs in the enterprise context.

About the Panelists

Animesh Singh, Executive Director, AI and ML Platform at LinkedIn

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.

Previously 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. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

Harrison Chase, CEO and Co-Founder of LangChain

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, CEO and Co-Founder of

BKetan Umare is the CEO and co-founder at 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, Senior Software Engineer, NVIDIA

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.

Stefan Lindall, Technologist

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.

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