Data Engineering Foundations for Enterprise AI Systems

High-quality AI models are only as good as the data that feeds them. For enterprises looking to deploy reliable AI systems, building a robust data foundation is the most critical step. This text-based course guides you through the essential data strategies and modern architectures needed to prepare enterprise information for generative AI. You will transition from understanding raw data formats to structuring high-performing data pipelines. What you'll learn: - Understand foundational data preparation, cleaning, and structuring techniques for AI systems. - Learn the mechanics of vector databases and how they store and retrieve high-dimensional data. - Apply Retrieval-Augmented Generation (RAG) patterns to connect enterprise knowledge to AI models. - Master metadata tagging and data governance practices to ensure secure and compliant AI outputs. - Explore modern data pipeline architectures designed to handle unstructured enterprise documents. Starting with key terminology and data quality principles, you will progress through structured text lessons covering vector search, chunking strategies, and RAG integration. This course is designed for beginners, data enthusiasts, and IT professionals looking to understand the data side of AI, requiring no prior experience in machine learning. Begin building the clean data foundations that modern enterprise AI systems depend on today.