According to @DeepLearningAI, a new short course with Qdrant teaches multi-vector image retrieval that directly matches text tokens to image patches and outperforms single-vector methods (source: DeepLearning.AI on X, Dec 10, 2025). According to @DeepLearningAI, the curriculum includes implementing ColBERT to understand multi-vector search fundamentals (source: DeepLearning.AI on X, Dec 10, 2025). According to @DeepLearningAI, learners will apply ColPali for patch-level image retrieval and reduce memory footprint via quantization and pooling (source: DeepLearning.AI on X, Dec 10, 2025). According to @DeepLearningAI, the course uses MUVERA to enable fast HNSW search and concludes with a full multimodal RAG pipeline built on ColPali and MUVERA (source: DeepLearning.AI on X, Dec 10, 2025).

According to DeepLearning.AI, Qdrant is sponsoring AI Dev 25, highlighting their contribution to advancing open-source vector search technology. Attendees are encouraged to visit their demo table to see their vector database in action and engage with the team at SHACK15 on Friday.