Data Engineering for AI: A Practical Guide for Data Professionals
Summary
Data engineering for AI demands new skills and a shift from traditional BI to managing large-scale, unstructured, and real-time data pipelines for ML and generative AI. Master feature engineering, vector databases, RAG, and ethical data practices alongside automation, observability, and unified data architecture to build production-grade AI solutions.
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