Data Engineering for Modern AI: From Pipeline to Model Input

Every successful artificial intelligence system relies on a foundation of clean, structured, and accessible data. This text-based course guides you through the essential process of preparing high-quality data inputs for AI models, moving from raw datasets to production-ready pipelines. You will understand how modern AI systems consume data and how to build the infrastructure that supports them. By completing this course, you will transform from a data novice into a practitioner capable of designing robust data pipelines and selecting the right storage solutions for AI applications. What you'll learn: - Understand the foundational concepts of data quality, preprocessing, and exploratory data analysis. - Design and build robust data pipelines that clean and transform raw data for AI consumption. - Configure and query vector databases to support retrieval-augmented generation and semantic search. - Apply modern data versioning techniques to keep track of changes in your training sets. - Implement secure and compliant data handling practices that protect user privacy in AI systems. The course begins with fundamental terminology, core data structures, and foundational definitions before moving step-by-step into data ingestion, exploratory analysis, vector storage, and pipeline automation. This structured path ensures you build a solid theoretical understanding before applying your knowledge to practical pipeline design. This course is designed for beginners, aspiring data engineers, and software developers looking to transition into AI-focused roles, with no prior data engineering experience required. Start reading today to master the data foundations that power modern artificial intelligence.