Overview
Explore the essential data frameworks powering generative AI systems, from LLMs to RAG and agentic AI. Master strategies for structured data management, governance principles, and taxonomy design to build reliable, ethical AI solutions.
Syllabus
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- Understanding Modern Data Strategy Fundamentals
-- In today’s rapidly evolving AI landscape, data is no longer just a byproduct—it's the fuel that powers intelligent systems. This module introduces the foundational role of data frameworks in modern data strategy, especially in the context of Generative AI applications. We begin by discussing how Large Language Models (LLMs), RAG (Retrieval-Augmented Generation), and Agentic AI systems rely on high-quality, well-governed data. You’ll explore the evolution of these technologies, their dependencies on structured and unstructured data, and how data strategy must evolve in parallel. The module also covers the core pillars of a modern data strategy—data frameworks, management, and governance—and explains their critical role in driving performance, compliance, and scalability in GenAI solutions. Through examples, case studies, and guided walkthroughs, you’ll learn how to design frameworks that support relevance, quality, and accountability, ensuring that AI systems are both powerful and responsible.
- Introduction to a Comprehensive Data Frameworks
-- As generative AI continues to evolve, the importance of well-structured data frameworks has become central to building scalable and ethical AI systems. In this module, we focus on designing comprehensive data frameworks that support the needs of modern AI systems, especially those that rely on both structured and unstructured data. You’ll explore the role of customized taxonomies in organizing data, and how these taxonomies enable consistent data classification and retrieval. We also examine how Responsible AI (RAI) principles influence data strategy and governance, ensuring that fairness, transparency, and accountability are built into the foundation. Through practical discussions and expert insights, you'll see how the components of a robust data framework—taxonomy design, ethical considerations, and governance practices—work together. Finally, we look ahead at emerging trends and evolving expectations in data frameworks to prepare for the future of GenAI deployment.
Taught by
Fractal Analytics and David Drummond
Tags
On-Demand
