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title	page_title	description	content	partition
Qdrant Essentials Course	Qdrant Essentials Course	Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.	sidebarTitle menuTitle nextButton nextDay title description Qdrant Essentials text url Course Overview /course/essentials/ Continue to Next Step Complete Qdrant Essentials Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.	course

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.

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• icon: /icons/outline/play-white.svg title: 7 days of lessons content: Short, focused videos with hands‑on exercises
• icon: /icons/outline/cloud-check-blue.svg title: Shareable certificate content: Earn a digital certificate upon completion
• icon: /icons/outline/time-blue.svg title: Flexible schedule content: Learn at your own pace (1–2 hours/day)
• icon: /icons/outline/plan.svg title: Beginner level content: No prior Qdrant experience required

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What you'll learn

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• 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

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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

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• icon: /icons/outline/training-purple.svg title: Video-first lessons content: Clear, concise modules by the Qdrant team
• icon: /icons/outline/hacker-purple.svg title: Final project content: Ship a production-ready vector search app
• icon: /icons/outline/similarity-blue.svg title: Bonus day content: Explore partner integrations on Day 7
• icon: /icons/outline/copy.svg title: Pitstop projects content: Small builds each day to apply the concept {{< /cards-list >}}

Syllabus

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• title: "Day 0: Setup and First Steps" content: |

  • Qdrant Cloud Setup
  • Implementing a Basic Vector Search
  • Project: Building Your First Vector Search System
    
    

  → Start Day 0

• title: "Day 1: Vector Search Fundamentals" content: |

  • Points, Vectors and Payloads
  • Distance Metrics
  • Text Chunking Strategies
  • Demo: Semantic Movie Search
  • Project: Building a Semantic Search Engine
    
    

  → Start Day 1

• title: "Day 2: Indexing and Performance" content: |

  • HNSW Indexing Fundamentals
  • Combining Vector Search and Filtering
  • Demo: HNSW Performance Tuning
  • Project: HNSW Performance Benchmarking
    
    

  → Start Day 2

• title: "Day 3: Hybrid Search" content: |

  • 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
    
    

  → Start Day 3

• title: "Day 4: Optimization and Scale" content: |

  • Vector Quantization Methods
  • Accuracy Recovery with Rescoring
  • High-Throughput Data Ingestion
  • Project: Quantization Performance Optimization
    
    

  → Start Day 4

• title: "Day 5: Advanced APIs" content: |

  • Multivectors for Late Interaction Models
  • The Universal Query API
  • Demo: Universal Query for Hybrid Retrieval
  • Project: Building a Recommendation System
    
    

  → Start Day 5

• title: "Day 6: Final Project - Building a Production-Grade Search Engine" content: |

  • Project Architecture and Evaluation Framework
  • Implementation and Performance Evaluation
  • Course Summary and Next Steps
    
    

  → Start Day 6

• title: "Day 7: Partner Ecosystem Integrations (Bonus)" content: |

  • AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
  • Data Processing (Unstructured.io)
  • ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)
    
    

  → Start Day 7

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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

{{< course-card title="Ready to start your vector search journey?" image="/icons/outline/rocket-white-light.svg" link="/course/essentials/day-0/">}} What you’ll get

• Build a production-ready docs search engine
• Practice with real projects
• Learn performance tuning techniques
• Portfolio artifacts and community support {{< /course-card >}}