Dive deep into the architecture of Weaviate, a vector database, in this comprehensive seminar talk presented by Etienne Dilocker as part of the CMU Database Group's ML⇄DB Seminar Series. Explore the inner workings of this innovative database system designed to handle vector data efficiently. Learn about the key components, design decisions, and technical challenges involved in building a scalable and performant vector database. Gain insights into how Weaviate addresses the unique requirements of machine learning applications and similarity search. Understand the trade-offs and optimizations made to ensure fast query processing and efficient storage of high-dimensional vector data. Discover how Weaviate integrates with existing machine learning workflows and supports various use cases in artificial intelligence and data science.