Speaker
Abstract
In this presentation, we will delve into the world of Retrieval Augmented Generation (RAG) and its significance for Large Language Models (LLMs) like OpenAI's GPT4. With the rapid evolution of data, LLMs face the challenge of staying up-to-date and contextually relevant. However, by harnessing the capabilities of vector embeddings and databases, LLMs can overcome these challenges and unlock their true potential.
Large Language Models, such as GPT4, are at the forefront of AI-driven advancements in natural language processing. To ensure their continued effectiveness, these models must adapt to ever-changing information. Vector embeddings, a powerful tool, are capable of capturing the essence of unstructured data. By combining these embeddings with sophisticated database search algorithms, LLMs gain access to a wealth of contextually relevant knowledge.
Topics
QCon San Francisco 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
Modern ML: GenAI, Trust, & Path2Prod Hosted by Hien Luu AI/ML Leader, Advisor, Speaker, and AuthorFrom the same track
Tuesday 3 October
10:35 Seacliff ABC Session AI/ML Chronon - Airbnb’s End-to-End Feature Platform Nikhil Simha Author of "Chronon Feature Platform", Previously Built Stream Processing Infra @Meta and NLP Systems @Amazon & @Walmartlabs ML Models typically use upwards of 100 features to generate a single prediction. As a result, there is an explosion in the number of data pipelines and high request fanout during prediction. 11:45 Seacliff ABC Session AI/ML Defensible Moats: Unlocking Enterprise Value with Large Language Models Nischal HP Vice President of Data Science @Scoutbee, Decade of Experience Building Enterprise AI Building LLM-powered applications using APIs alone poses significant challenges for enterprises. These challenges include data fragmentation, the absence of a shared business vocabulary, privacy concerns regarding data, and diverse objectives among data and ML users. 13:35 Ballroom A Session Distributed Computing Modern Compute Stack for Scaling Large AI/ML/LLM Workloads Jules Damji Lead Developer Advocate @Anyscale, MLflow Contributor, and Co-Author of "Learning Spark" Advanced machine learning (ML) models, particularly large language models (LLMs), require scaling beyond a single machine. 14:45 Pacific DEKJ Session AI/ML Generative Search: Practical Advice for Retrieval Augmented Generation (RAG) Sam Partee Principal Engineer @Redis In this presentation, we will delve into the world of Retrieval Augmented Generation (RAG) and its significance for Large Language Models (LLMs) like OpenAI's GPT4. With the rapid evolution of data, LLMs face the challenge of staying up-to-date and contextually relevant. 15:55 Seacliff D Unconference Unconference: Modern ML 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. 17:05 Ballroom BC Session AI/ML Building Guardrails for Enterprise AI Applications W/ LLMs Shreya Rajpal Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure Large Language Models (LLMs) such as ChatGPT have revolutionized AI applications, offering unprecedented potential for complex real-world scenarios. However, fully harnessing this potential comes with unique challenges such as model brittleness and the need for consistent, accurate outputs.