Watch a 21-minute technical talk exploring how Large Language Models (LLMs) are revolutionizing data workflows and analytics processes. Learn from Ines Chami, Chief Scientist and Co-Founder of Numbers Station, as she draws from her Stanford University research and industry experience to demonstrate how Foundation Models can streamline ETL processes, automate text-to-SQL generation, and enhance data visualization. Gain practical insights into deploying these advanced models in production environments, understanding their capabilities in data extraction, cleaning, and analytics automation. Follow along through key segments covering LLM fundamentals, their integration into modern data stacks, applications in analytics workflows, data preparation processes, and their transformative impact on the future of data science. Perfect for data scientists, engineers, and AI practitioners seeking to modernize their data operations with cutting-edge language models.
Leveraging Large Language Models for ETL, Analytics, and Modern Data Stack
Overview
Syllabus
- Introduction
- What are LLMs?
- How can we use LLMs in the Modern Data Stack
- LLMs for analytics workflows
- LLMs for data preparation workflows
- Reshaping the future
Taught by
Open Data Science
