Building Knowledge Stores with Azure AI Search

Organizations are flooded with unstructured data like PDFs, images, and documents, leaving valuable insights locked away. Learning to extract, enrich, and store this information is key to building intelligent search applications and data pipelines. This text-only course guides you through the entire process of setting up Azure AI Search to extract deep insights and save them into a persistent knowledge store. You will learn to define data sources, configure enrichment cognitive skills, and map the outputs to structured tables and files for analysis or integration with modern AI patterns. What you'll learn: 1. Understand the foundational concepts of Azure AI Search and knowledge store architectures. 2. Configure cognitive skillsets to perform image processing, key phrase extraction, and entity recognition. 3. Define projection mappings to save enriched data into Azure Table and Blob storage. 4. Explore modern integration patterns, including preparing data for retrieval-augmented generation (RAG). 5. Query and analyze persistent knowledge store projections using standard tools. You will start with core definitions and architectural concepts before moving step-by-step through configuring data sources, defining skillsets, and projecting data. The course concludes with practical scenarios for using your stored knowledge in downstream applications. This course is designed for beginner data engineers, cloud developers, and AI enthusiasts who want to learn data enrichment, with no prior experience with Azure AI Search required. Start reading today to unlock the hidden value in your unstructured documents.