According to DeepLearning.AI on Twitter, a new short course in collaboration with Qdrant introduces AI professionals to advanced multi-vector image retrieval techniques. Led by Senior Developer Advocate Kacper Lukawski from Qdrant, the course demonstrates how multi-vector search methods, such as ColBERT and ColPali, surpass traditional single-vector approaches by directly matching text tokens to image patches. Participants will learn practical implementation of ColBERT for multi-vector search, use ColPali for patch-level image retrieval, apply quantization and pooling to optimize memory usage, and leverage MUVERA for efficient HNSW-based searches. The curriculum culminates in building a full multi-modal RAG (Retrieval-Augmented Generation) pipeline, showcasing real-world applications and business opportunities in scalable, high-performance AI-powered image retrieval. (Source: DeepLearning.AI, Twitter)