updated videos, landing pages, certification

2026-10-04 10:28:29 +02:00 · 2026-03-14 02:19:04 -07:00
parent d51e2b40b2
commit 7cc1341185
17 changed files with 65 additions and 25 deletions
@@ -146,11 +146,11 @@ ML, backend, and search engineers who want to go beyond single-vector embeddings

 ## Time commitment

- Duration: 4 modules at 2-3 hours/module
- Video learning: ~4 hours
- Hands-on notebooks: ~4 hours
- Final project: 2-4 hours
- Total: 8-12 hours
+- Duration: 3 modules at 1 hour/module
+- Video learning: 1.5 hours
+- Hands-on notebooks: 1.5 hours
+- Final project: 1-3 hours
+- Total: 4-6 hours


 {{< course-card
@@ -0,0 +1,25 @@
+---
+title: "Qdrant Multi-Vector Certification"
+description: "Get officially certified in multi-vector search by Qdrant."
+url: /course/multi-vector-search/certification/
+weight: 50
+---
+
+# Qdrant Multi-Vector Search Certification
+
+Congratulations! You’ve completed the **Multi-Vector Search course**. You didn’t just learn how to store vectors; you learned how to build high-performance retrieval systems using late interaction models and multi-vector representations.
+
+You’ve moved past single-vector embeddings and dove deep into ColBERT, ColPali, MaxSim scoring, MUVERA, and production-grade multi-vector pipelines. That effort deserves more than just a “finished” status. It deserves professional recognition!
+
+## Get #QdrantCertified
+
+Your expertise is now production-ready. It’s time to validate those skills with our official certification.
+
+Head over to [train.qdrant.dev](https://train.qdrant.dev) to take the exam.
+
+Passing this exam proves you aren’t just a user; you are a Search Engineer capable of:
+
+- Designing multi-vector retrieval pipelines with late interaction models.
+- Applying ColPali and its variants for visual document search.
+- Optimizing multi-vector search for both memory and latency.
+- Mastering MaxSim scoring and the nuances of multi-vector architectures in Qdrant.
@@ -16,7 +16,7 @@ This lesson introduces the late interaction paradigm - the foundation of multi-v

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/8HrvD5o2w8M?rel=0"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ Understanding MaxSim is important for working with multi-vector search effective

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/JvSvuK19m8A?rel=0"
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  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -18,7 +18,7 @@ By the end, you'll understand the key configuration parameters, know when to use

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/THZE2O4kMDg?rel=0"
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  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ The good news: Module 3 covers optimization techniques that address many of thes

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/vQKF1hO7hzU?rel=0"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ The answer comes down to one core capability: **fine-grained matching**. In the

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/35WO2_Q_0C0?rel=0"
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@@ -16,7 +16,7 @@ Let's explore what the options are and which model to choose depending on the da

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/5ypH0t_X-4k?rel=0"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ Understanding ColPali's architecture helps you leverage its full potential for m

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/Fai9aY1PMCA?rel=0"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ This visual interpretability is invaluable for building trust in multi-modal sea

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/sQcuYWMS4bo?rel=0"
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  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -18,7 +18,7 @@ The answer lies in systematic evaluation across three dimensions: **cost** (memo

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/1gHbp9c01iE?rel=0"
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@@ -114,7 +114,7 @@ These hints are optional. Feel free to chart your own path.

 ## Share Your Results

-We'd love to see what you build. Share your project on the [Qdrant Discord](https://discord.gg/qdrant) in the `#courses` channel.
+We'd love to see what you build. Share your project on the [Qdrant Discord](https://discord.gg/qdrant) in the [#course-submissions](https://discord.com/channels/907569970500743200/1429673887590776832) channel.

 Tell us about:

@@ -16,7 +16,7 @@ Qdrant's Universal Query API makes it easy to build sophisticated multi-stage re

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/qIjPepsY35E?rel=0"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ Understanding MUVERA is key to scaling multi-vector search to millions of docume

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/-r0Apuy0c8k?rel=0"
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  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -14,7 +14,7 @@ While quantization reduces the size of each vector, pooling reduces the number o

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  src="https://www.youtube-nocookie.com/embed/idDXBOrIuik?rel=0"
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  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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@@ -16,7 +16,7 @@ Choosing the right quantization method depends on your quality requirements and

 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/xK9mV7zR4pL"
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