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.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.

Please click here to see the transcript of this episode.

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