An embedding is a concept in machine learning that refers to a particular representation of text, images, audio, or other information. Embeddings are designed to make data consumable by ML models.
However, storing embeddings presents a challenge to traditional databases. Vector databases are designed to solve this problem.
Marek Galovic is a software engineer at Pinecone and works on the core database team. He joins the podcast today to talk about how vector embeddings are created, engineering a vector database, unsolved challenges in the space, and more.
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