This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional software engineering and MLOps by structuring how machine learning should be researched, decoupled, trained, and integrated. It covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.