Embeddings are compact representations of high-dimensional data that help in explaining complex relationships between different inputs. They play a crucial role in machine learning, particularly in reducing the dimensionality of input features and facilitating collaboration across teams. Despite their potential to simplify data, embeddings can still be challenging to understand without additional techniques like UMAP.