updated videos, landing pages, certification

qdrant-landing/content/course/_index.md

@@ -1,5 +1,5 @@
 ---
-title: "Welcome to Qdrant Academy"
+title: "Qdrant Academy"
 description: Master vector search and AI-powered applications with Qdrant Academy. Free, self-paced courses guide you from beginner to expert with hands-on projects, code notebooks, and certification.
 weight: 50
 ---
@@ -12,13 +12,11 @@ Qdrant Academy is your step-by-step learning hub for mastering vector search, hy
 
 Whether you’re new to Qdrant or building production-grade systems, our guided courses help you go from beginner to expert, one module at a time.
 
-Qdrant Academy has just launched in Fall of 2025 and currently offers one comprehensive course, but more are on the way! Register your interest in each upcoming below.
-
 ## Available Now
 
-{{< course-card 
+{{< course-card
  title="Qdrant Essentials Course"
- image="/icons/outline/rocket-white-light.svg" 
+ image="/icons/outline/rocket-white-light.svg"
  link="/course/essentials/"
 >}}
 **What you’ll gain:**
@@ -30,7 +28,24 @@ Qdrant Academy has just launched in Fall of 2025 and currently offers one compre
 - Ecosystem Integrations (Bonus)
 <br><br>
 Time to Complete: 9-12 hours<br>
-Includes: videos, code notebooks, projects, walkthroughs
+Includes: videos, code notebooks, projects, certification
+{{< /course-card >}}
+
+{{< course-card
+ title="Multi-Vector Search Course"
+ image="/icons/outline/similarity-blue.svg"
+ link="/course/multi-vector-search/"
+>}}
+**What you’ll gain:**
+- Late Interaction Models and MaxSim Scoring
+- ColBERT for Text Search
+- ColPali for Visual Document Search
+- Multi-Stage Retrieval Pipelines
+- Quantization and Pooling Techniques
+- MUVERA Indexing for Large-Scale Search
+<br><br>
+Time to Complete: 4-6 hours<br>
+Includes: videos, code notebooks, projects, certification
 {{< /course-card >}}
 
 ## Upcoming Courses

qdrant-landing/content/course/multi-vector-search/_index.md

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

qdrant-landing/content/course/multi-vector-search/certification.md

@@ -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.

qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md

@@ -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"
   referrerpolicy="strict-origin-when-cross-origin"

qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md

@@ -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"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"

qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md

@@ -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"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md

@@ -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"
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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md

@@ -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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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md

@@ -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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qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md

@@ -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"
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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md

@@ -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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qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md

@@ -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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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-3/final-project.md

@@ -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:
 

qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md

@@ -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"
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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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qdrant-landing/content/course/multi-vector-search/module-3/muvera.md

@@ -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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qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md

@@ -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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qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md

@@ -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"
+  src="https://www.youtube-nocookie.com/embed/we-AEfiXaow?rel=0"
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