Machine learning (ML) deals with algorithms able to learn from data, with the primary aim of finding optimum solutions to perform tasks autonomously. In recent years there has been development in integrating ML algorithms with live coding practices, raising questions about what to optimize or automate, the agency of the algorithms, and in which parts of the ML processes one might intervene midperformance. Live coding performance practices typically involve conversational interaction with algorithmic processes in real time. In analyzing systems integrating live coding and ML, we consider the musical and performative implications of the “moment of intervention” in the ML model and workflow, and the channels for real-time intervention. We propose a framework for analysis, through which we reflect on the domain-specific algorithms and practices being developed that combine these two practices.