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Reimagining Data Science with Generative AI (Mexico Time)

Webinar Speaker: James Kobielus, Senior Research Director for Data Management

Date: Tuesday, May 14, 2024

Time: 10:00 a.m. CST

Generative AI is transforming the way enterprise data scientists extract insights from data and deliver these insights to business teams. To accelerate analytics endeavors, data science teams are beginning to use LLMs that generate SQL to interact with structured data. In parallel, business counterparts now expect answers related to their private enterprise data to be delivered through conversational interfaces such as AI chatbots. Because of this, AI teams need to explore new approaches such as retrieval-augmented generation to reduce hallucinations and anchor generative AI outputs in trusted enterprise knowledge sources.

Join us to hear James Kobielus, TDWI senior research director, discuss how generative AI tools are transforming the practice of data science. Kobielus will be joined Snowflake’s Caleb Baechtold, principal AI architect, who will discuss the associated opportunities and challenges for enterprise data science professionals. Their discussion will cover:

  • What are the principal applications of generative AI in modern data science?
  • How are generative AI tools boosting the productivity of today’s data scientists in writing SQL code, building machine learning models, and other tasks?
  • What is the role of data scientists in supporting new architecture patterns such as retrieval-augmented generation to anchor the outputs of generative AI applications in enterprise knowledge sources and single version of the truth?
  • What guardrails, governance, and other controls should enterprises be building into their data science teams’ use of generative AI?

Guest Speaker

Caleb Baechtold
Principal AI Architect, Field CTO

Caleb is an experienced data scientist and technical project lead with extensive knowledge of the defense and intelligence contracting industry. Skilled in traditional modeling and simulation techniques, machine learning and artificial intelligence, and systems architecting and integration. Experience forming and driving data science and AI/ML enterprise strategies in addition to implementing technical solutions in client environments. Technical professional with a MA and BA in Mathematics, and BA in Philosophy from The Johns Hopkins University.

James Kobielus

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