In this podcast episode, Chris Churilo (VP of Marketing) and James Luan (VP of Engineering at Zilliz) discuss the advancements and features of Milvus, a scalable vector database designed for unstructured data. They delve into the latest version, Milvus 2.6, highlighting its focus on cost reduction, scalability, and integration with AI applications. The conversation covers various topics, including tiered storage, quantization techniques, and the introduction of a new logging solution called Woodpecker. Additionally, they explore the importance of embedding models and user-defined functions, as well as optimizations in scalar search and phrase matching.

To learn more about working with unstructured data, visit ⁠⁠zilliz.com⁠⁠ and ⁠⁠milvus.io⁠⁠