How AI Agents Are Transforming Data Analytics

Join the MLOps Community - https://mlops.community/join Integrating Large Language Models (LLMs) into production-level data workflows presents both significant challenges and opportunities. In this talk, we'll introduce Numbers Station, a platform that automates data analytics workflows using LLMs, Retrieval Augmented Generation (RAG) over a Knowledge Layer, and a customizable multi-agent architecture. We'll start by discussing practical use cases for analytics, such as dashboard search, query generation, or automatically adding analysis summaries to slide deck presentations. We'll then delve into the methodologies for deploying LLMs within data analytics workflows, focusing on a detailed case study to build a SQL agent from the ground up. We will cover the architectural considerations necessary to support agent-based analytics, including the role of dynamic control flows and the importance of incorporating business context through a unified Knowledge Layer. This session aims to provide a deep technical insight into transforming theoretical AI frameworks into practical, scalable solutions that advance organizational data capabilities. //Bio Ines Chami is the Chief Scientist and Co-Founder of Numbers Station. She received her Ph.D. in the Institute for Computational and Mathematical Engineering from Stanford University where she was advised by Prof. Christopher Ré. Prior to attending Stanford, she studied Mathematics and Computer Science at Ecole Centrale Paris. Ines is particularly excited about building intelligent models to automate data-intensive work. Her work spans applications such as knowledge graph construction and data cleaning. For her work on graph representation learning, she won the 2021 Stanford Gene Golub Doctoral Dissertation Award. During her Ph.D. she interned at Microsoft AI and Research and Google Research where she co-authored the graph representation learning chapter of Kevin Murphy's "Probabilistic Machine Learning: An Introduction" book