Vector Databases: Embeddings, Indexing, Search & Deployment

Vector databases power today's AI-driven search, recommendation, and RAG (retrieval-augmented generation) systems — but understanding how they actually work under the hood is a different skill than just plugging one into an app. This course is built as a rigorous, comprehensive practice-test series designed to test and reinforce your knowledge across every layer of vector database technology, from first principles to production operations.You'll work through six full practice tests, each covering a distinct area:Vector Database Fundamentals & Core Concepts — embeddings, distance metrics, collections, CRUD operations, and core terminologyIndexing Algorithms & ANN Search — HNSW, IVF, LSH, product/scalar/binary quantization, and the recall-latency tradeoffVector Database Architecture & Data Management — sharding, replication, consistency models, ingestion pipelines, and query executionQuery Optimization & Search Techniques — hybrid search, BM25, re-ranking, filtering, multi-modal search, and evaluation metrics like precision, recall, MRR, and NDCGPopular Vector Database Platforms & Comparisons — Pinecone, Weaviate, Milvus, Qdrant, Chroma, and how to evaluate deployment models, licensing, and total cost of ownershipProduction Deployment, Scaling & Advanced Operations — deployment strategies, disaster recovery, security, cost optimization, and team operational maturityEach question includes a detailed explanation connecting the concept to related ideas covered elsewhere in the course, so you're not just memorizing facts — you're building a genuinely connected mental model of how vector databases work end to end.Whether you're a software engineer building a RAG pipeline, a data scientist evaluating platforms, or preparing for a technical interview touching on AI infrastructure, this course will help you validate your understanding and identify gaps before they matter in a real project or interview.