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Anupam Datta

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Principal Research Scientist in AI - Snowflake

Anupam Datta is a Principal Research Scientist in AI at Snowflake. He served as Co-Founder, President, and Chief Scientist of TruEra from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022, most recently as Professor of Electrical & Computer Engineering and Computer Science. Datta's 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 Value, 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. He obtained Ph.D. and M.S. degrees from Stanford University and a B.Tech. from IIT Kharagpur, all in Computer Science.

Bio from: Big Data LDN 2024

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In this flagship Big Data LDN keynote debate, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists will debate the impact of Generative AI, the implications of key industry trends, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, how to manage, produce, share and govern data and AI, and issues on-the-horizon that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data-driven enterprise from seasoned executives and an experienced industry analyst in a packed, unscripted, candid discussion.

As organisations shift from generative AI proof of concepts to building production ready applications, the requirements for efficiency, monitoring, safety and governance become critical to both trust and success.

You will learn:

Key design patterns and methodology for evaluating, experimenting and monitoring enterprise gen AI apps to address common failure modes

The role of iteration and improvement as part of ongoing delivery

Practical considerations for implementation using examples from Snowflake’s Cortex Analyst, Cortex Search and TruLens, an open source project.