New DeepLearning.AI Course on Multi-Vector Image Retrieval with ColPali and MUVERA

We’re thrilled to announce our latest collaboration with DeepLearning.AI: Multi-Vector Image Retrieval. Building on the success of our previous course on retrieval optimization, this intermediate-level course takes you deeper into advanced search techniques that are transforming how AI systems understand and retrieve visual content.

Led once again by Qdrant’s Kacper Łukawski, Senior Developer Advocate, this free course is designed for AI builders working with multi-modal data who want to implement cutting-edge image retrieval in their applications.

Why This Collaboration Matters

Our continued partnership with DeepLearning.AI reflects our commitment to advancing the field of vector search through education. While our first course introduced developers to the fundamentals of retrieval optimization, this intermediate course tackles one of the most challenging problems in modern AI: fine-grained matching between text queries and visual content.

Multi-vector approaches represent a significant leap forward from traditional single-vector embeddings. By encoding images as multiple vectors, one for each visual patch, these techniques enable far more precise and nuanced search capabilities. This is particularly powerful for applications requiring detailed visual understanding, from e-commerce product search to document analysis.

What You’ll Learn

This course provides comprehensive coverage of multi-vector retrieval techniques and their practical implementation:

• Understand the fundamentals of multi-vector embeddings and how patch-level representations dramatically improve search accuracy compared to single-vector approaches.
• Remind ColBERT for text retrieval and learn how its late-interaction architecture enables fine-grained semantic matching.
• Master ColPali, a vision language model that generates multi-vector representations for images, enabling detailed text-to-image search.
• Learn optimization techniques including quantization and pooling to minimize memory requirements while maintaining search quality.
• Discover MUVERA’s approach to converting high-dimensional multi-vector embeddings into efficient representations for production systems.
• Build complete multi-modal RAG pipelines that combine ColPali retrieval with HNSW search algorithms for real-world applications.

Who Should Enroll

This course is designed for AI builders working with multi-modal data who want to implement advanced image retrieval systems. You should have foundational familiarity with Python and vector embeddings to get the most from this intermediate-level content.

Whether you’re building visual search applications, enhancing document retrieval systems, or developing multi-modal AI assistants, this course provides the practical knowledge you need to implement state-of-the-art retrieval techniques.

At a Glance:

• Speaker: Kacper Łukawski, Senior Developer Advocate at Qdrant
• Level: Intermediate
• Cost: Free
• Location: Online
• Duration: 1 hour 33 minutes

How to Enroll

Enroll via the DeepLearning.AI website and start building advanced multi-vector retrieval systems today.