MC09: Intro to Vector Embedding and Vector Database

Modern AI applications increasingly rely on vector embeddings and vector databases to retrieve relevant information from large collections of data. This masterclass introduces the foundational concepts behind semantic search, embeddings, and how vector databases power many of today’s LLM-based applications such as ChatGPT plugins, AI assistants, and RAG systems.

In this session, participants will learn how unstructured data (documents, PDFs, knowledge bases, websites, etc.) can be transformed into embeddings and stored inside vector databases to enable intelligent retrieval and contextual AI responses.
Through practical examples, the masterclass will explain how embeddings represent meaning mathematically and how vector similarity search helps AI systems find the most relevant information efficiently.

This session is particularly valuable for anyone interested in building AI-powered applications that can search, understand, and reason over private data sources.

This masterclass is part of the AI Residency.

✅ Join the new AI Residency cohort to build this end-to-end with guided support, project feedback, and a production-ready workflow—from data ingestion → indexing → retrieval → evaluation → deployment.
https://academy.decodingdatascience.com/airesidencyfasttrack