Despite recent tendencies towards building large "monolithic" neural models, fine-tuned expert models and parameter-efficient specialised modules still offer gains over large monoliths in specific tasks and for specific data distributions (e.g., low-resource languages or specialised domains). Moreover, such modularisation of skills and expertise into dedicated models or modules allows for asynchronous, decentralised, and more efficient continuous model development, as well as module reusability. However, a central question remains: how to combine and compose these modules to enable positive transfer, sample-efficient learning, and improved out-of-domain generalisation. In this talk, after discussing the key advantages of modularisation and modular specialisation, I will provide an overview of prominent module and model composition strategies. I will focus on composition at the parameter level (model merging) and functional level (model MoErging), and then illustrate the usefulness of these techniques across several applications.