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.