This paper presents a multidisciplinary case study of practice with machine learning for computer music. It builds on the scientific study of two machine learning models respectively developed for data-driven sound synthesis and interactive exploration. It details how the learning capabilities of the two models were leveraged to design and implement a musical interface focused on embodied musical interaction. It then describes how this interface was employed and applied to the composition and performance of aego, an improvisational piece with interactive sound and image for one performer. We discuss the outputs of our research and creation process, and expose our personal reflections and insights on transdisciplinary research opportunities framed by machine learning for computer music.