Speaker
Abstract
Our panel is a conversation that aim to explore the practical and operational challenges of implementing LLMs in production. Each of our panelists will share their experiences and insights within their respective organizations.
The panel will provide a view of the current state of LLMs in production environments. Our goal is to stimulate thoughtful conversation and exchanges of ideas among AI researchers, software engineers and tech leaders. We strive to foster a nuanced understanding of the current landscape of LLMs in production and anticipate its future directions.
QCon New York 2023 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
MLOps: Navigating the Terrain of Large-Scale Models Hosted by Bozhao (Bo) Yu Founder @BentoML.aiFrom the same track
Wednesday 14 June
10:35 Salon D Session ML Infrastructure Introducing the Hendrix ML Platform: An Evolution of Spotify’s ML Infrastructure Divita Vohra, Mike Seid The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem. 11:50 Salon D Session Machine Learning Improve Feature Freshness in Large Scale ML Data Processing Zhongliang Liang Engineering Manager @Facebook AI Infra In many ML use cases, model performance is highly dependent on the quality of the features they are trained and inference on. One of the important dimensions of feature quality is the freshness of the data. 13:40 Carroll Gardens Unconference Unconference: MLOps What is an unconference? An unconference is a participant-driven meeting. Attendees come together, bringing their challenges and relying on the experience and know-how of their peers for solutions. 14:55 Salon D Session AI/ML A Bicycle for the (AI) Mind: GPT-4 + Tools Sherwin Wu, Atty Eleti OpenAI recently introduced GPT-3.5 Turbo and GPT-4, the latest in its series of language models that also power ChatGPT. 16:10 Salon D Session MLOps Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products Massimo Belloni Data Science Manager @Bumble In this talk, we would go through the lessons learnt in the last couple of years around organising a Data Science Team and the Machine Learning Engineering efforts at Bumble Inc. 17:25 Salon D Panel Panel: Navigating the Future: LLM in Production Sherwin Wu, Hien Luu, Rishab Ramanathan Our panel is a conversation that aim to explore the practical and operational challenges of implementing LLMs in production. Each of our panelists will share their experiences and insights within their respective organizations.