mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-08 12:28:32 +02:00
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>
110 lines
3.7 KiB
Markdown
110 lines
3.7 KiB
Markdown
---
|
||
title: Qdrant Essentials Course
|
||
page_title: Qdrant Essentials Course
|
||
description: The ultimate guide to production-grade vector search is here. And it’s free.
|
||
content:
|
||
sidebarTitle: Qdrant Essentials
|
||
menuTitle:
|
||
text: Course Overview
|
||
url: /course/essentials/
|
||
getStarted:
|
||
text: Get Started
|
||
url: /course/essentials/day-0/
|
||
nextButton: Continue to Next Video
|
||
nextDay: Complete
|
||
title: Qdrant Essentials Course
|
||
description: The ultimate guide to production-grade vector search is here. And it’s free.
|
||
partition: course
|
||
---
|
||
|
||
# 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
|
||
|
||
{{< accordion >}}
|
||
- title: "Days 0: Setup, Orientation & “Hello Qdrant!”"
|
||
content: |
|
||
- Welcome & Course Orientation
|
||
- Environment Setup
|
||
- Mini “Hello Qdrant!” Demo
|
||
|
||
- title: "Day 1: Core Qdrant Data Model & Vector Search 101"
|
||
content: Content
|
||
|
||
- title: "Days 2: Indexing & Vector Storage Architecture"
|
||
content: Content
|
||
|
||
- title: "Day 3: Hybrid Search"
|
||
content: Content
|
||
|
||
- title: "Day 4: Optimizations & Query APIs"
|
||
content: Content
|
||
{{< /accordion >}}
|
||
|
||
## Certificate of Completion
|
||
|
||
image
|
||
|
||
## 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
|
||
|
||
{{< /course-card >}}
|