Data modeling and artificial intelligence (AI) are deeply connected, yet they often evolve in parallel without enough integration. Data models shape the structure, meaning, and quality of the data that fuels AI, while AI models depend on well-organized, well-understood data to produce reliable outcomes.

The Data Modeling for AI course bridges that gap. It provides a comprehensive look at how modern data modeling principles support AI development – from semantic and conceptual modeling to governance, bias testing, and explainability.

Led by Dave Wells, a leading voice in data management and analytics strategy, this two-day, live online course explores how to model data for AI readiness, feature generation, and interpretability. You’ll learn how evolving data models support both discriminative AI (which classifies and predicts) and generative AI (which creates and learns).

Through lectures, examples, and hands-on exercises, you’ll discover how to:

• Apply data modeling principles across the AI lifecycle
• Connect data governance and ethics to AI quality and accountability
• Understand data bias, AI bias, and hallucination—and how to mitigate them
• Design models for semantic consistency, lineage, and explainability
• Incorporate modern architectures and data types that power generative AI

This course provides a clear framework for understanding how data modeling contributes to AI success, and how to evolve your modeling practices for the next generation of intelligent systems.

Program Highlights

Over two days of immersive learning, you’ll:

• Explore the evolving role of data modeling in AI and machine learning
• Learn how data modeling supports business understanding and feature engineering
• Examine semantic, conceptual, logical, and physical models for modern data ecosystems
• Identify the touchpoints between AI modeling and data modeling throughout the AI lifecycle
• Discuss AI governance, bias mitigation, and explainability frameworks
• Analyze the roles of data engineers, data scientists, and modelers in AI collaboration
• Examine the impact of generative AI, large language models (LLMs), and GraphRAG on modeling practices

By the End of This Course, You’ll Be Able To:

• Describe the relationship between data models and AI models
• Apply modeling techniques for feature engineering and data preparation
• Design and document models that improve AI interpretability and transparency
• Identify and address bias and hallucination risks in AI systems
• Integrate AI governance and explainability into data modeling practices
• Collaborate effectively across data and AI teams to ensure alignment

How the Course Works

This two-day, live online workshop combines instructor-led sessions, Q&A, and practical exercises led by Dave Wells. Each segment builds understanding through real-world examples, frameworks, and guided breakout discussions.

During the live sessions, you’ll experience:

• Interactive lectures covering data modeling and AI integration principles
• Group discussions and breakout sessions to apply new concepts
• Hands-on exercises in modeling for AI readiness and governance
• Live Q&A and instructor feedback throughout
• Continued access to recordings and resources after the course

All content and materials are hosted in the DATAVERSITY Training Center (DVTC), where you’ll also join the live Zoom sessions.

Schedule:

• Dates: Tuesday–Wednesday, March 10–11, 2026
• Time: 8 AM – 3 PM Pacific / 11 AM – 6 PM Eastern
• CEUs: 12

Pricing:

• $1,000 Early Bird (through February 20, 2026)
• $1,200 Regular Price (after February 20, 2026)

Registration Deadline: Sunday, March 8, 2026

Why Choose This Course

• Led by Dave Wells, a respected authority on data management, analytics, and architecture
• Integrates data modeling, AI, and governance into one cohesive learning experience
• Provides frameworks, examples, and reusable templates for immediate application
• Explores cutting-edge topics such as Explainable AI, AI bias, Generative AI, and GraphRAG
• Designed for both technical and business professionals working in AI-driven environments

Is This Course Right for You or Your Team?

This course is ideal for:

• Data and enterprise architects modernizing their data ecosystems for AI
• Data scientists, engineers, and analysts who want stronger modeling foundations
• Managers, consultants, and project leaders guiding AI or data strategy initiatives
• Teams seeking to connect data modeling and AI development for improved alignment and trust

No prior AI or data modeling certification is required, just familiarity with organizational data and an interest in designing smarter, more explainable AI solutions.

Private Team Training

Organizations can host private two-day sessions to explore how data modeling supports AI within their unique contexts. Corporate packages include instructor facilitation, customized examples, and group exercises.

Contact us to explore corporate training options or bulk licensing.

Backed by Industry Leaders

Delivered through the DATAVERSITY Training Center (DVTC), this course brings together the principles of modern data management and the realities of artificial intelligence.

It equips professionals to bridge the gap between data modeling discipline and AI innovation—building a foundation for transparency, trust, and sustainable AI success.