What is embedding models?

Embedding models are machine learning models that map discrete objects (e.g., words, sentences, images, or users) into continuous vector spaces. These vectors capture semantic relationships between the objects, allowing for tasks like similarity search, recommendation, and classification. They are commonly used to represent data in a way that machine learning algorithms can easily process and understand, improving the performance of various AI applications.