Chicago CDAO Town Hall
June 18, 2024 | 11:00am CDT
June 18, 2024 | 11:00am CDT
Unlock Your Organization’s Knowledge with Retrieval-Augmented Generation
Participate with your CDAO peers in the Chicago community in small, breakout discussions on a mission-critical priority for data leaders. These informal discussions allow you to share your insights and learn from the lessons, approaches and experiences of a diverse group of peers in virtual breakout rooms.
Join fellow CDAOs in the Chicago community to discuss:
Foster a Strong Data and Analytics Function Through Leadership and Influence
Restructuring Data Strategy and Governance to Responsibly Deploy Generative AI
Enhance Data Literacy Across the Organization to Drive Business Value
Discussion Leaders
Members of the Chicago CDAO community will lead the interactive discussion and share lessons learned.
Igor Jablokov
Pryon
CEO
Presenter
Garima Sharma
Amrock
Vice President, Data Science
Governing Body Host
Patrick Chew
AIT Worldwide Logistics
Global Head Data Enablement
Discussion Leader
Gokula Mishra
Direct Supply
VP, Data Science & AI/ML
Discussion Leader
Jai Prakash
Pella
Head of Insights, Data Engineering and Analytics
Discussion Leader
Agenda
June 18, 2024
11:00am - 12:00pm CDT
Unlock Your Organization’s Knowledge with Retrieval-Augmented Generation
Hosted by Pryon
Retrieval-Augmented Generation (RAG) has emerged as a powerful capability to help businesses efficiently access their trusted enterprise knowledge, while mitigating a number of the challenges associated with generative AI (e.g., hallucinations, lack of up-to-date information, and lack of domain-specific knowledge). An enterprise RAG architecture sets your business on the path to developing its own Knowledge Cloud, serving up valuable knowledge to users when and where they need it. However, while the concept of RAG is straightforward, implementing and scaling it for an enterprise introduces complex challenges.
Join this session to discuss:
- How Generative AI is revolutionizing the future of knowledge-based work
- Core requirements for building an enterprise-ready RAG solution
- Ways organizations can effectively deploy RAG-based AI solutions
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