Add course overview

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

@@ -1,7 +1,7 @@
 ---
 title: "Multi-Vector Search Course"
 page_title: "Qdrant Multi-Vector Search Course"
-description: TBD
+description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
 content:
   sidebarTitle: "Multi-Vector Search Course"
   menuTitle:
@@ -10,10 +10,156 @@ content:
   nextButton: Continue to Next Step
   nextDay: Complete
   title: "Multi-Vector Search"
-  description: tbd
+  description: Master late interaction models, ColPali, and production optimization. Build scalable multi-vector search pipelines.
 partition: course
 ---
 
 # Multi-Vector Search
 
-TBD
+**Build production-ready multi-vector search pipelines**
+
+Go beyond single-vector embeddings with late interaction models like ColBERT and ColPali. Learn the MaxSim distance metric, optimize for billion-scale search, and evaluate your retrieval pipelines with industry-standard metrics.
+
+<div class="video">
+<iframe
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen>
+</iframe>
+</div>
+
+<br/>
+
+{{< cards-list >}}
+- icon: /icons/outline/play-white.svg
+  title: 4 modules
+  content: Focused lessons building from fundamentals to production
+- icon: /icons/outline/cloud-check-blue.svg
+  title: Shareable certificate
+  content: Earn a digital certificate upon completion
+- icon: /icons/outline/time-blue.svg
+  title: Flexible schedule
+  content: Learn at your own pace (1–2 hours/module)
+- icon: /icons/outline/plan.svg
+  title: Advanced level
+  content: Assumes familiarity with vector search basics
+
+{{< /cards-list >}}
+
+<br/>
+
+## What you'll learn
+{{< course-card
+ title="Skills you'll gain:"
+ image="/icons/outline/training-white.svg"
+ type="wide-list">}}
+
+- Late interaction paradigm and MaxSim distance metric
+- ColBERT for text and ColPali for visual documents
+- Multi-stage retrieval with prefetch and reranking
+- Quantization and pooling techniques for memory optimization
+- MUVERA indexing for billion-scale search
+- Evaluation metrics: Recall@k, NDCG, MRR
+
+{{< /course-card >}}
+
+### The Path
+
+**Module 0**: Setup. Configure Qdrant Cloud or local instance and install Python dependencies.
+
+**Module 1**: Text multi-vectors. Understand the late interaction paradigm, learn the MaxSim distance metric, explore use cases and challenges, and implement ColBERT with Qdrant.
+
+**Module 2**: Multi-modal search. Apply multi-vector representations to images and PDFs with ColPali. Explore model variants and leverage visual interpretability for debugging.
+
+**Module 3**: Optimization and evaluation. Master quantization, pooling, and MUVERA for memory-efficient search. Build multi-stage retrieval pipelines and evaluate with standard metrics.
+
+## How the course works
+
+{{< cards-list >}}
+
+- icon: /icons/outline/training-purple.svg
+  title: Video-first lessons
+  content: Clear, concise modules by the Qdrant team
+- icon: /icons/outline/hacker-purple.svg
+  title: Final project
+  content: Build a production-ready multi-modal search system
+- icon: /icons/outline/similarity-blue.svg
+  title: Hands-on notebooks
+  content: Practice each concept with Colab notebooks
+- icon: /icons/outline/copy.svg
+  title: Progressive learning
+  content: Build from fundamentals to advanced optimization
+  {{< /cards-list >}}
+
+<br/>
+
+## Syllabus
+
+{{< accordion >}}
+- title: "Module 0: Setting Up Dependencies"
+  content: |
+    - Qdrant Setup
+    - Installing Dependencies
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-0/">→ Start Module 0</a></p>
+
+- title: "Module 1: Multi-Vector Representations for Textual Data"
+  content: |
+    - Late Interaction Basics
+    - MaxSim Distance Metric
+    - Use Cases for Multi-Vector Search
+    - Problems of Multi-Vector Search
+    - Multi-Vector Embeddings in Qdrant
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-1/">→ Start Module 1</a></p>
+
+- title: "Module 2: Multi-Vector Representations for Multi-Modal Data"
+  content: |
+    - How ColPali Models Work
+    - ColPali Family Overview
+    - Visual Interpretability of ColPali
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-2/">→ Start Module 2</a></p>
+
+- title: "Module 3: Scalability and Optimization"
+  content: |
+    - Multi-Stage Retrieval with Universal Query API
+    - Vector Quantization Techniques
+    - Pooling Techniques
+    - MUVERA Indexing
+    - Evaluating Search Pipelines
+    - Final Project
+    <br>
+    <br>
+    <p style="margin-left: 0px;"><a href="/course/multi-vector-search/module-3/">→ Start Module 3</a></p>
+{{< /accordion >}}
+
+
+## Who it's for
+
+ML, backend, and search engineers who want to go beyond single-vector embeddings. Requires intermediate Python, basic familiarity with vector search concepts (embeddings, similarity metrics), and comfort with APIs.
+
+## 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
+
+
+{{< course-card
+ title="Ready to master multi-vector search?"
+ image="/icons/outline/rocket-white-light.svg"
+ link="/course/multi-vector-search/module-0/">}}
+**What you'll get**
+- Build production-ready multi-vector pipelines
+- Practice with real Colab notebooks
+- Learn optimization techniques for scale
+- Portfolio project and community support
+{{< /course-card >}}