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67 lines
4.2 KiB
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67 lines
4.2 KiB
Markdown
---
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title: "Master Multi-Vector Search With Qdrant"
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draft: false
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slug: multi-vector-search-course
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short_description: "Go beyond single-vector embeddings. Our new advanced course covers ColBERT, ColPali, MaxSim, and production-grade multi-vector pipelines in Qdrant."
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description: "Go beyond single-vector embeddings. Our new advanced course covers ColBERT, ColPali, MaxSim, and production-grade multi-vector pipelines in Qdrant."
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preview_image: /blog/multi-vector-course-release/hero.png
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social_preview_image: /blog/multi-vector-course-release/hero.png
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date: 2026-03-24
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author: Neil Kanungo
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featured: true
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tags:
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- qdrant-course
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- multi-vector-search
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- colbert
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- colpali
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- certification
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---
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Most vector search tutorials stop at single-vector embeddings: one document, one vector, one similarity score. That works for demos. It falls apart when your retrieval pipeline needs to capture fine-grained token-level interactions across text, images, and PDFs at production scale.
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Until now, engineers who wanted to go deeper had to piece together scattered papers, blog posts, and half-documented repos. There was no structured, hands-on resource that connected the theory of late interaction models to real implementation in a production search engine.
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We built one.
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## Introducing the Multi-Vector Search Course
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[Qdrant's Multi-Vector Search Course](https://qdrant.tech/course/multi-vector-search/) is a free, advanced course created by [Kacper Łukawski](https://www.linkedin.com/in/kacperlukawski/). Kacper designed this course to fill a real gap in the developer community: practical, production-focused education on multi-vector retrieval that goes well beyond "here's how embeddings work."
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This is an advanced course! It's built for ML engineers, backend engineers, and search engineers who already understand vector search fundamentals and want to master what comes next.
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## What You'll Learn
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The course is organized into four modules, each taking roughly one to two hours:
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**Module 0: Setup.** Configure a Qdrant Cloud or local instance and install Python dependencies.
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**Module 1: Text Multi-Vectors.** Understand the late interaction paradigm, learn the MaxSim distance metric, explore real use cases and challenges, and implement ColBERT-based search with Qdrant.
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**Module 2: Multi-Modal Search.** Apply multi-vector representations to images and PDFs using ColPali. Explore model variants in the ColPali family and use visual interpretability for debugging retrieval results.
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**Module 3: Optimization and Evaluation.** Master vector quantization, pooling techniques, and MUVERA indexing for memory-efficient search at billion scale. Build multi-stage retrieval pipelines with Qdrant's Universal Query API and evaluate with industry-standard metrics (Recall@k, NDCG, MRR).
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The course wraps up with a final project: build your own production-ready multi-modal search system from scratch.
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## How It Works
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Every module combines video lessons from the Qdrant team with hands-on Google Colab notebooks. You watch, you build, you evaluate. The progressive structure means each module builds directly on the previous one, so you finish with a complete, working pipeline rather than a collection of disconnected concepts.
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## Earn a Qdrant Certification
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Complete the course and pass the certification exam to earn a shareable Qdrant Multi-Vector Search certificate. It's a concrete way to demonstrate that you can design, implement, and optimize multi-vector retrieval pipelines in production.
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Certifications are available through [Qdrant Academy](https://qdrant.tech/course/multi-vector-search/certification/).
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## Free Swag for the First 20 Certified
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Dive in now: the **first 20 people** who complete their Multi-Vector Search certification and post on LinkedIn with the hashtag **#QdrantCertified** will receive free Qdrant swag. Share your certificate, tag us, and we'll reach out.
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## Start Now
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The course is free, self-paced, and available today. If you've been looking for a structured path from "I understand embeddings" to "I can build and evaluate multi-vector retrieval at scale," this is it.
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[Start the Multi-Vector Search Course](https://qdrant.tech/course/multi-vector-search/)
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Questions? Join our [Discord community](https://discord.gg/qdrant) where Qdrant experts collaborate.
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