The text discusses embedding models, which are machine learning models designed to represent data in a continuous, low-dimensional vector space. These models capture semantic or contextual similarities between pieces of data, enabling machines to perform tasks like comparison, clustering, or classification more effectively. Embedding models can be used for various applications such as text search, movie recommendations, image matching, and grouping similar items. There are several types of embedding models, including word embedding models, contextualized word embedding models, sentence or document embedding models, image embedding models, and audio embedding models. Each model is designed to handle specific types of data and tasks, helping to capture and represent relationships usefully for machine learning applications. The training process involves collecting and preparing data, choosing a training objective, using neural networks, backpropagation and optimization, and evaluating the model's performance. Choosing the right embedding model depends on factors such as data type, task requirements, performance considerations, size of dataset, and pre-trained models vs. custom training.