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AI Deep Dive: AI-Ready Data Framework with Agent Skills (Virtual)

Event artwork for AI Deep Dive: AI-Ready Data Framework with Agent Skills (Virtual)

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About this event

What to expect

This virtual AI Deep Dive session, hosted by the São Paulo AI Developer Group in partnership with Snowflake, tackles one of the most critical yet overlooked challenges in AI development: making your data truly AI-ready. If you've ever wondered why your AI initiatives underperform despite having access to vast amounts of data, this session will provide the diagnostic framework and practical solutions you need.

The Core Problem This Event Addresses

Most AI projects fail not because of poor models or insufficient compute power, but because they're built on data that isn't prepared for the demanding requirements of modern AI workloads. The result is hallucinations, weak retrieval performance, wasted engineering effort, and ultimately broken trust in AI systems. This session introduces the AI-Ready Data Framework, an open standard that helps teams systematically assess data readiness and apply targeted remediations.

What You'll Learn

Jacob Prall from Snowflake will guide you through three critical areas:

  • Diagnosing data problems behind AI failures: Learn practical techniques for identifying when poor AI performance stems from data quality issues rather than model architecture. You'll gain a diagnostic lens for spotting readiness gaps that cause hallucinations, weak retrieval, and other common problems.

  • Setting appropriate standards for different workloads: Understand that RAG systems, autonomous agents, and model training each have distinct data requirements. You'll learn how to prioritize investments based on your specific use case.

  • Closing the readiness gap: Discover the practical approaches successful teams use to address data-readiness gaps once identified, including a suite of agent Skills designed to assess data, identify problems, and apply fixes.

Who Should Attend

This session is ideal for AI developers, data engineers, ML practitioners, and technical leaders working with RAG systems, agentic AI, or custom model training. Whether you're troubleshooting existing AI implementations or planning new initiatives, you'll gain actionable frameworks for ensuring your data meets the demands of production AI workloads.

Format and Accessibility

This is a virtual event accessible to the global AI community, so verify your local time zone (2:00 PM BRT on April 15, 2026). Can't attend live? Register anyway to receive the recording. The session

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