In this episode, Chris Churilo and Jay Byoun discuss the intricacies of vector databases, particularly focusing on Zilliz and Milvus. They explore the performance and scale requirements for vector databases, the challenges customers face in production, and the importance of understanding vector counts. The conversation delves into user experience, query challenges, and the balance between performance, cost, and accuracy. They also touch on hybrid search, metadata filtering, and the future of vector databases in relation to data lakes and security considerations.

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

In this conversation, Stefan Webb (Developer Advocate) and Jay Byoun (Solutions Architect) discuss the intricacies of Zilliz Cloud and vector databases, focusing on indexing algorithms, recall and latency trade-offs, chunking strategies, cost considerations in LLM inference, and diverse use cases for vector databases. They also provide insights on how to get started with Zilliz, emphasizing the importance of understanding these concepts for effective data management and application development.

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