nastyapashandgenerall
d758ddcb81
* Add Course page * Added shortcodes and buttons to navigate to the next page * move course images from layout * decouple documentation and course layout * decouple course sidebar * move accordion js handlers into global unconditional js * enable search on courses * allow nested courses * disable links for now --------- Co-authored-by: generall <andrey@vasnetsov.com>
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| Qdrant Essentials Course | Qdrant Essentials Course | The ultimate guide to production-grade vector search is here. And it’s free. |
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Qdrant Essentials Course
The ultimate guide to production-grade vector search is here. And it’s free.
From your first vector upsert to optimizing high-performance retrieval at scale, this free course takes you from zero to production-ready. Learn how to build efficient vector search, fine-tune Qdrant for maximum performance, and keep your system lightweight, even when working with billions of vectors.
{{< course-card title="Skills you’ll gain:" image="/icons/outline/training-white.svg" type="wide-list">}}
- Vector search fundamentals
- Performance optimization
- Hybrid and similarity search
- Portfolio project development
{{< /course-card >}}
What Is the Course?
No matter if you're exploring vector search for the first time or fine-tuning a large-scale RAG system, this free course gives you the practical foundation and advanced skills you need.
Over 9 days (plus bonus content), you’ll build up from the fundamentals to advanced deployment strategies with Qdrant. Each module focuses on a single concept or capability, paired with a hands-on exercise to apply what you’ve learned. You will start with basics, build confidence, and gradually progress to complex topics.
Every day includes a hands-on exercise or mini-project, like creating a collection, uploading points, building a hybrid search pipeline, or tuning the HNSW index.
At the end, you’ll bring everything together by building a full production-grade vector search application. You’ll graduate with a portfolio-worthy project, plus a deep understanding of how to apply Qdrant in production scenarios.
Course Overview
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title: "Days 0: Setup, Orientation & “Hello Qdrant!”" content: |
- Welcome & Course Orientation
- Environment Setup
- Mini “Hello Qdrant!” Demo
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title: "Day 1: Core Qdrant Data Model & Vector Search 101" content: Content
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title: "Days 2: Indexing & Vector Storage Architecture" content: Content
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title: "Day 3: Hybrid Search" content: Content
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title: "Day 4: Optimizations & Query APIs" content: Content {{< /accordion >}}
Certificate of Completion
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Who Is the Course For?
You! But really, this course is great for hands-on professionals who need to build or improve applications with semantic or hybrid search capabilities, or developers exploring vector databases for the first time.
If your job title includes:
- Machine Learning Engineer
- Backend Developer
- Data Engineer
- Search Engineer
- MLOps Engineer
you’re in the right spot.
Pre-Reqs
You don’t need prior Qdrant or vector database experience, but you should be comfortable with:
- Basic Python programming
- Running commands in your terminal
- Working with APIs or Python SDKs
- Some ML background (e.g., embeddings)
Optional but helpful:
- Docker basics
- Experience with search systems or deploying applications
{{< course-card title="Why Start Today" image="/icons/outline/rocket-white-light.svg" link="/course/day-0/">}}
- Seeing practical examples (e.g., hybrid search, sparse+dense vectors)
- Learning key deployment tactics (multi-node clusters, on-disk indexing, RBAC)
- Building a final portfolio-grade project to showcase
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