You are viewing content from a past/completed conference.
Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
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.
Speaker
Sam Partee
Principal Engineer @Redis
Sam Partee is a principal engineer at Redis helping lead the development and awareness of Redis in machine learning systems. Sam has a background in high performance computing and he previously worked at Cray and HPE on projects like SmartSim, Chapel, and DeterminedAI. In his spare time, Sam enjoys contributing to open source projects, writing on his blog, and spending time with friends and family.
Find Sam Partee at:
From the same track
Session
AI/ML
Chronon - Airbnb’s End-to-End Feature Platform
Tuesday Oct 3 / 10:35AM PDT
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.
Nikhil Simha
Author of "Chronon Feature Platform", Previously Built Stream Processing Infra @Meta and NLP Systems @Amazon & @Walmartlabs
Session
AI/ML
Defensible Moats: Unlocking Enterprise Value with Large Language Models
Tuesday Oct 3 / 11:45AM PDT
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.
Nischal HP
Vice President of Data Science @Scoutbee, Decade of Experience Building Enterprise AI
Session
Distributed Computing
Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Tuesday Oct 3 / 01:35PM PDT
Advanced machine learning (ML) models, particularly large language models (LLMs), require scaling beyond a single machine.
Jules Damji
Lead Developer Advocate @Anyscale, MLflow Contributor, and Co-Author of "Learning Spark"
Session
AI/ML
Building Guardrails for Enterprise AI Applications W/ LLMs
Tuesday Oct 3 / 05:05PM PDT
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.
Shreya Rajpal
Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure
Session
Unconference: Modern ML
Tuesday Oct 3 / 03:55PM PDT
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.
Unconference: Modern ML
Slides are not available