This chapter explores semantic search, emphasizing the use of image content rather than visual matches, showcasing how image embeddings from models like clip can facilitate unexpected feature matching. Additionally, there is an ad read for a low code AI pipeline builder named Plumb, outlining its advantages and functionalities.
Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.
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