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DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2025.data
 versity.net/sessionPop.cfm?confid=164&proposalid=15984\nData modeling is an
  essential skill data management skill set that has an important (but frequ
 ently unrecognized) role in artificial intelligence. Data modeling and AI a
 re both evolving fields. Everyone is aware of AI evolution because it is at
  the forefront of technology news. It seems that the evolution of data mode
 ling is a well-kept secret. Data modeling of the past was aligned with the 
 design of relational databases. Today, data modeling addresses many data ty
 pes — relational, key-value, wide-column, document, graph, and more &
 mdash; at all levels from business semantics to design and implementation.\
 n\nAI models depend on data. Discriminative models classify existing data a
 nd use it to infer predictions and conclusions. Generative models create ne
 w data that is collected, stored, managed, and used as feedback. Data model
 ing provides techniques to organize, understand, prepare, and manage data f
 or AI. Data models provide business context, describe data content and orga
 nization, support feature engineering and data preparation, and reinforce m
 odel interpretability.\n\nJoin us for this session to learn:\n\n
 The variety of data model types and techniques for modern data modeling\n
 Four levels of data model abstraction\n
 Six phases of the AI Lifecycle and the activities of each phase\n
 Where and how Data Modeling fits into the AI Lifecycle\n
 The roles of data models in AI Governance and Explainable AI\n\n
DTSTART:20250505T083000
SUMMARY:T3: Data Modeling for AI
DTEND:20250505T114459
LOCATION: See Description
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