// Abstract
Large language models are fluent text generators, but they often make errors, which makes them difficult to deploy in high-stakes applications. Using them in more complicated pipelines, such as retrieval pipelines or agents, exacerbates the problem. In this talk, Matei will cover emerging techniques in the field of “LLMOps” - how to build, tune and maintain LLM-based applications with high quality. The simplest tools are ones to test and visualize LLM results, some of which are now being incorporated into MLOps frameworks like MLflow. However, there are also rich techniques emerging to “program” LLM pipelines and control LLMs’ outputs to achieve desired goals.
Matei discusses Demonstrate-Search-Predict (DSP) from my group as an example programming framework that can automatically improve an LLM-based application based on feedback, and other open-source tools for controlling outputs and generating better training and evaluation data for LLMs. This talk is based on their experience deploying LLMs in many applications at Databricks, including the QA bot on our public website, internal QA bots, code assistants, and others, all of which are making their way into our MLOps products and MLflow.
// Bio
Matei Zaharia is a Co-founder and Chief Technologist at Databricks as well as an Assistant Professor of Computer Science at Stanford. He started the Apache Spark project during his Ph.D. at UC Berkeley in 2009 and has worked broadly on other widely used data and AI software, including MLflow, Delta Lake, Dolly, and ColBERT. He works on a wide variety of projects in data management and machine learning at Databricks and Stanford. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).
8 сен 2024
