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.

Pinecone has developed one of the most prominent vector databases that is widely used for ML and AI applications.

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.