YouTube uses vectors to analyze and recommend videos with similar content by converting video data into numeric representations via machine learning models called embeddings.
These vectors are stored in specialized systems known as vector databases designed to efficiently manage high-dimensional vectors at scale.
Vector databases support functions like semantic similarity search, enabling AI applications to deliver smart recommendations and experiences without custom retrieval logic.
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.