Special Data Base Seminar - Anupam Datta
— 3:00pm
Location:
Virtual Presentation - ET
-
Remote Access - Zoom
Speaker:
ANUPAM DATTA
,
Principal Research ScientistSnowflake AI Research LeadSnowflake
https://www.snowflake.com/en/blog/authors/anupam-datta/
Snowflake’s Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at production scale poses fundamental challenges. Semantic operations are more expensive than traditional SQL operations, possess distinct latency and throughput characteristics, and their cost and selectivity are unknown during query compilation. Furthermore, existing query engines are not designed to optimize semantic operations.
The AISQL query execution engine addresses these challenges through three novel techniques informed by production deployment data from Snowflake customers. First, AI-aware query optimization treats AI inference cost as a first-class optimization objective, reasoning about large language model (LLM) cost directly during query planning to achieve 2–8A— speedups. Second, adaptive model cascades reduce inference costs by routing most rows through a fast proxy model while escalating uncertain cases to a powerful oracle model, achieving 2–6A— speedups while maintaining 90–95% of oracle model quality. Third, semantic join query rewriting lowers the quadratic time complexity of join operations to linear through reformulation as multi-label classification tasks, achieving 15–70A— speedups with often improved prediction quality. AISQL is deployed in production at Snowflake, where it powers diverse customer workloads across analytics, search, and content understanding.
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Anupam Datta is a Principal Research Scientist and Snowflake AI Research Lead at Snowflake. He joined Snowflake as part of the acquisition of TruEra where he served as Co-Founder, President, and Chief Scientist from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022, most recently as a tenured Professor of Electrical & Computer Engineering and Computer Science. Datta's current research focuses on Trustworthy AI, spanning evaluation, explainability, fairness, and adversarial robustness of ML models and GenAI applications. Specific results include early work on Shapley Values & gradient-based explanations, fairness assessments, robustness of classical machine learning and deep learning models for natural language processing and computer vision, and the TruLens open source project for evaluation and experiment tracking of GenAI apps. These research results have had a significant impact on products at TruEra and Snowflake. Datta has published over 100 research papers, served as Chair of the National Academies Workshop on Assessing and Improving AI Trustworthiness, on the Steering Committee of of the ACM Conference on Fairness, Accountability, and Transparency, and the IEEE Computer Security Foundations Symposium, and as an Editor-in-Chief of Foundations and Trends in Privacy and Security.
He received the 2018 David P. Casasent Outstanding Research Award from the CMU College of Engineering, a 2020 Young Alumni Achiever Award from IIT Kharagpur, a 2021 Google Faculty Research Award, and several awards for top papers at conferences. Datta obtained a B.Tech. from IIT Kharagpur, and Ph.D. and M.S. degrees from Stanford University in Computer Science, where he currently teaches a course on Trustworthy AI.
Zoom Participation. See announcement.
For More Information:
db-www@cs.cmu.edu