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Building RAG-based LLM Applications for Production

Posted Oct 26, 2023 | Views 2K
# LLM Applications
# RAG
# Anyscale
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Yifei Feng
Engineering Manager @ Anyscale

Yifei leads the Infrastructure and SRE teams at Anyscale. Her teams focus on building a seamless, cost efficient and scalable infrastructure for large scale machine learning workloads. Before Anyscale, she spent a few years at Google working on open-source machine learning library TensorFlow.

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Philipp Moritz
Chief Technology Officer @ Anyscale

Philipp Moritz is one of the creators of Ray, an open-source system for scaling AI. He is also co-founder and CTO of Anyscale, the company behind Ray. He is passionate about machine learning, artificial intelligence, and computing in general and strives to create the best open-source tools for developers to build and scale their AI applications.

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Adam Becker
IRL @ MLOps Community

I'm a tech entrepreneur and I spent the last decade founding companies that drive societal change.

I am now building Deep Matter, a startup still in stealth mode...

I was most recently building Telepath, the world's most developer-friendly machine learning platform. Throughout my previous projects, I had learned that building machine learning powered applications is hard - especially hard when you don't have a background in data science. I believe that this is choking innovation, especially in industries that can't support large data teams.

For example, I previously co-founded Call Time AI, where we used Artificial Intelligence to assemble and study the largest database of political contributions. The company powered progressive campaigns from school board to the Presidency. As of October, 2020, we helped Democrats raise tens of millions of dollars. In April of 2021, we sold Call Time to Political Data Inc.. Our success, in large part, is due to our ability to productionize machine learning.

I believe that knowledge is unbounded, and that everything that is not forbidden by laws of nature is achievable, given the right knowledge. This holds immense promise for the future of intelligence and therefore for the future of well-being. I believe that the process of mining knowledge should be done honestly and responsibly, and that wielding it should be done with care. I co-founded Telepath to give more tools to more people to access more knowledge.

I'm fascinated by the relationship between technology, science and history. I graduated from UC Berkeley with degrees in Astrophysics and Classics and have published several papers on those topics. I was previously a researcher at the Getty Villa where I wrote about Ancient Greek math and at the Weizmann Institute, where I researched supernovae.

I currently live in New York City. I enjoy advising startups, thinking about how they can make for an excellent vehicle for addressing the Israeli-Palestinian conflict, and hearing from random folks who stumble on my LinkedIn profile. Reach out, friend!

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SUMMARY

In this talk, we will cover how to develop and deploy RAG-based LLM applications for production. We will cover how the major workloads (data loading and preprocessing, embedding, serving) can be scaled on a cluster, how different configurations can be evaluated and how the application can be deployed. We will also give an introduction to Anyscale Endpoints which offers a cost-effective solution for serving popular open-source models.

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