Agentic AI: Building Data-First AI Agents

In this course, Senior Staff Instructor Morten Rand-Hendriksen and Microsoft Director of Data & AI Strategy Viroopax Mirji analyze the data challenges enterprises face when building AI agents. The course examines data quality, privacy, integration, and bias, with real-world examples from industries like healthcare and autonomous vehicles. The conversation covers best practices for data management, governance, and transitioning to an AI-driven future, ensuring transparency and accountability in AI systems.

Learning objectives

• Analyze the biggest data challenges enterprises face when building AI agents.
• Examine critical aspects of data management, including data quality, privacy, integration, and bias.
• Illustrate the impact of poor data management on AI projects with real-world examples.
• Learn best practices for ensuring high-quality data, including data cleaning, transformation, and feature selection.
• Explore the importance of data governance and its key principles.
• Compare AI-centric data governance to traditional data governance.
• Provide strategies for transitioning to an AI agent-driven future.
• Understand the data challenges in AI implementation and the strategies to overcome them.