<ul data-eligibleForWebStory="false"><li>YouTube uses vectors to analyze and recommend videos with similar content by converting video data into numeric representations via machine learning models called embeddings.</li><li>These vectors are stored in specialized systems known as vector databases designed to efficiently manage high-dimensional vectors at scale.</li><li>Vector databases support functions like semantic similarity search, enabling AI applications to deliver smart recommendations and experiences without custom retrieval logic.</li><li>Popular vector databases, such as Milvus and Faiss, are commonly used for semantic search, recommendation engines, and image similarity tasks, showcasing their importance in AI applications.</li></ul>

The Secret Maps Behind YouTube and Spotify: Understanding Vectors and Vector Databases (Part 1)

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