In this talk I will discuss the pros and cons of various methods for learning feature representations for audio (e.g, music recommendation), text (e.g., retrieval, and syntactic and semantic tagging ), images (e.g., ranking and annotation) and knowledge (e.g. using the Wordnet graph to help in the tasks above). Particular emphasis is put on methods that scale well on large data and have fast serving times so that they can be used in production. In particular I have worked on a number of supervised feature embedding algorithms that work well on these tasks which I will describe, as well as areas where I think these methods can be improved.