Qdrant Essentials
Ship a production-ready docs search in 7 days
Build the vector search skills that matter: hybrid retrieval, multivector reranking, quantization, distributed deployment, and multitenancy. Ship a complete documentation search engine as your final project.
7 days of lessons
Short, focused videos with hands‑on exercises
Shareable certificate
Earn a digital certificate upon completion
Flexible schedule
Learn at your own pace (1–2 hours/day)
Beginner level
No prior Qdrant experience requiredWhat you’ll learn
Skills you'll gain:
- Qdrant data modeling: points, payloads, and schemas
- Embeddings, chunking, and similarity metrics
- Indexing and retrieval tuning (HNSW, filters, recall/latency)
- Hybrid search with sparse + dense vectors and re-ranking
- Performance optimization, compression, and quantization
- Scaling, sharding/replication, and security
The Path
Days 0-2: Foundations. Connect to Qdrant Cloud, work with points and payloads, compute semantic similarity, chunk text, and tune HNSW for speed and recall.
Days 3-5: Advanced retrieval. Combine dense and sparse signals, do hybrid search with server-side fusion, use multivectors (ColBERT) with the Universal Query API, and build recommendations.
Day 6: Ship. Wire ingestion, hybrid retrieval, multivector re-ranking, and evaluation (Recall@10, MRR, latency P50/P95).
Day 7 (bonus): Ecosystem. Try integrations with AI frameworks, search tools, and data pipelines.
How the course works
Video-first lessons
Clear, concise modules by the Qdrant team
Final project
Ship a production-ready vector search app
Bonus day
Explore partner integrations on Day 7
Pitstop projects
Small builds each day to apply the conceptSyllabus
Day 0: Setup and First Steps
- Qdrant Cloud Setup
- Implementing a Basic Vector Search
- Project: Building Your First Vector Search System
Day 1: Vector Search Fundamentals
- Points, Vectors and Payloads
- Distance Metrics
- Text Chunking Strategies
- Demo: Semantic Movie Search
- Project: Building a Semantic Search Engine
Day 2: Indexing and Performance
- HNSW Indexing Fundamentals
- Combining Vector Search and Filtering
- Demo: HNSW Performance Tuning
- Project: HNSW Performance Benchmarking
Day 3: Hybrid Search
- Sparse Vectors and Inverted Indexes
- Demo: Keyword Search with Sparse Vectors
- Hybrid Search with Score Fusion
- Demo: Implementing a Hybrid Search System
- Project: Building a Hybrid Search Engine
Day 4: Optimization and Scale
- Vector Quantization Methods
- Accuracy Recovery with Rescoring
- High-Throughput Data Ingestion
- Project: Quantization Performance Optimization
Day 5: Advanced APIs
- Multivectors for Late Interaction Models
- The Universal Query API
- Demo: Universal Query for Hybrid Retrieval
- Project: Building a Recommendation System
Day 6: Final Project - Building a Production-Grade Search Engine
- Project Architecture and Evaluation Framework
- Implementation and Performance Evaluation
- Course Summary and Next Steps
Day 7: Partner Ecosystem Integrations (Bonus)
- AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
- Data Processing (Unstructured.io)
- ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)
Who it’s for
ML, backend, data, and search engineers building RAG, semantic search, or recommendations. Requires intermediate Python, basic CLI/APIs, and familiarity with embeddings.
Time commitment
- Duration: 6 days at 1-2 hours/day + 1 optional bonus day
- Video learning: ~3 hours
- Hands-on learning: 4-5 hours
- Final project: 2-4 hours
- Total: 9-12 hours
Ready to start your vector search journey?
What you’ll get
- Build a production-ready docs search engine
- Practice with real projects
- Learn performance tuning techniques
- Portfolio artifacts and community support
