Module 5Course Summary
This module completes the Qdrant Beginners course. Here’s what was covered:
| Module | Theme | Key concepts covered |
|---|---|---|
| Module 1 | Let’s Understand Search | Why keyword search fails; how embeddings and semantic search work; the shift from words to meaning. |
| Module 2 | First Principles of Vector Search | Collections, points, vectors, payloads, HNSW, chunking strategies, and the full ingestion pipeline. |
| Module 3 | Sparse vs Dense vs Hybrid Search | BM25 against embeddings; when each fails; hybrid search with rank fusion. |
| Module 4 | Designing a Vector Search System | The five layers of the stack; what to decide before ingesting; what changes as the collection grows; when to add machines; where generation fits; where to run Qdrant. |
| Module 5 | Multimodal Supplier Risk Intelligence | End-to-end capstone: ingest news, transcripts, and images on shared points; cluster risk signals; query every modality. |
| Module 6 | Beyond Similarity (Bonus) | Optional further reading: score boosting, MMR diversity, two-stage reranking, grouping, relevance feedback, and discovery. |
Next, get #QdrantCertified with the official Beginners exam, which covers Modules 1 through 5.