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68 lines
4.8 KiB
Markdown
68 lines
4.8 KiB
Markdown
---
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title: "Qdrant Academy Expands with Official Certification"
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draft: false
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slug: qdrant-certification-launch
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short_description: "Qdrant Academy launched with first course, Qdrant Essentials"
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description: "Master the art of production-grade retrieval with Qdrant Academy’s new certification. Earn credentials, score exclusive swag, and level up your engineering skills."
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preview_image: /blog/qdrant-certification-launch/hero-graphic.png
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social_preview_image: /blog/qdrant-certification-launch/hero-graphic.png
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date: 2026-01-28
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author: Neil Kanungo
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featured: true
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tags:
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- Community
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- Academy
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---
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Since we first announced **[Qdrant Academy](https://qdrant.tech/course/)**, our mission has been to provide developers with more than just documentation. We wanted to build a structured path to mastering vector search. As the AI search landscape matures, the distinction between a simple storage layer and a high-performance vector search engine has become the defining factor in production-grade RAG and recommendation systems.
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Today, we are thrilled to take the next step in that mission. It’s time to move from learning to proving your expertise with the launch of our first official certification.
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### Introducing the "Qdrant Essentials" Certification
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The [Qdrant Essentials course](https://qdrant.tech/course/essentials/) has already helped thousands of developers understand the "why" behind high-dimensional search. Now, you can officially validate that knowledge.
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By completing the course and passing the final exam at **[train.qdrant.dev](https://train.qdrant.dev)**, you’ll earn a digital credential that proves you can architect search systems that are as efficient as they are accurate.
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#### What the Essentials Track Covers:
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* **Engine Architecture:** Deep dives into HNSW, distance metrics, and collection structures.
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* **Precision Filtering:** Mastering payload-based filtering without sacrificing search speed.
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* **Hybrid Search:** Implementing a mix of dense and sparse vectors for superior retrieval.
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* **Production Optimization:** Utilizing quantization and rescoring to scale your engine efficiently.
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### Why Get Certified?
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In a field as fast-moving as AI, "knowing a bit of Python" isn't enough. Moving from a prototype to a production-ready system requires specialized engineering judgment. Becoming **#QdrantCertified** can be a game-changer for your career:
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* **Verified Expertise:** It proves you understand the critical trade-offs—like balancing latency vs. accuracy—that separate a hobbyist project from enterprise infrastructure.
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* **Career Differentiation:** As companies hunt for RAG and Agentic AI experts, this badge signals that you can handle high-scale vector search, reducing your onboarding time and making you an immediate asset.
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* **Standardized Knowledge:** You aren't just learning from assorted tutorials; you’re learning the industry standard for high-performance retrieval directly from the creators of Qdrant.
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* **Engineering Authority:** Gain the confidence to lead internal AI workshops or architect your company's next-gen search platform using verified best practices.
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### Get Certified. Get Swag.
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We want to see those certificates! To celebrate the launch of our certification platform, we’re sending out some exclusive gear to our early achievers.
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> **The first 30 people** to post their Qdrant Essentials certification to LinkedIn with the hashtag **#QdrantCertified** will receive a free Qdrant swag pack.
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It’s simple: Learn, pass the exam at [train.qdrant.dev](https://train.qdrant.dev), and share your success with the community to claim your prize.
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### More Courses Launching Soon
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The "Essentials" course is just the foundation. Qdrant Academy is expanding rapidly to support developers at every stage of their journey:
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#### The 2-Hour Beginner Launchpad
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Coming soon, we are launching a **2-hour Basic Course**. This is designed for those who need a high-impact, low-time-commitment introduction to the world of vector search. You’ll go from "What is an embedding?" to "I have a running search engine" in a quick yet comprehensive Qdrant intro.
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#### Advanced Retrieval Topics
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For the power users, our upcoming **Multivectors Course** will tackle the cutting edge of retrieval. It will focus on Late Interaction models (like ColBERT), and will cover sophisticated retrieval with MUVERA. You’ll learn how to handle token-level embeddings to achieve incredible retrieval precision for complex datasets.
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## Ready to Level Up?
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Come grow with Qdrant, and prove your knowledge with Qdrant Certifications:
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1. **Learn:** Head over to the [Qdrant Essentials course](https://qdrant.tech/course/essentials/).
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2. **Certify:** Take the exam and claim your badge at **[train.qdrant.dev](https://train.qdrant.dev)**.
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3. **Win:** Post it on LinkedIn with **#QdrantCertified** and grab your swag.
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As always, happy coding! |