September 2026
Intermediate to advanced
100 pages
1h 7m
English
Data Management for AI and Agentic Systems offers a foundational guide to designing robust data pipelines for AI models and agents. As demand for AI-driven decision-making grows, many systems fail because of poor or unreliable data. This book focuses on a critical—but often overlooked—component of success: structured, trustworthy, and context-rich data.
Author Michael Bachman draws on decades of experience to bridge the gap between data engineering and AI development. With practical strategies, case studies, and real-world code examples, readers will learn to align AI and data systems for greater accuracy, scalability, and performance in production environments.
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