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:

1. Vector Database Fundamentals & Core Concepts — embeddings, distance metrics, collections, CRUD operations, and core terminology

2. Indexing Algorithms & ANN Search — HNSW, IVF, LSH, product/scalar/binary quantization, and the recall-latency tradeoff

3. Vector Database Architecture & Data Management — sharding, replication, consistency models, ingestion pipelines, and query execution

4. Query Optimization & Search Techniques — hybrid search, BM25, re-ranking, filtering, multi-modal search, and evaluation metrics like precision, recall, MRR, and NDCG

5. Popular Vector Database Platforms & Comparisons — Pinecone, Weaviate, Milvus, Qdrant, Chroma, and how to evaluate deployment models, licensing, and total cost of ownership

6. Production Deployment, Scaling & Advanced Operations — deployment strategies, disaster recovery, security, cost optimization, and team operational maturity

Each 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.