ML Adoption Playbook provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It structures problem framing, data readiness, architectural decoupling, model implementation, and MLOps handoff to bridge the gap between traditional software engineering and machine learning. Reach for it when you need a structured workflow to add machine learning capabilities to a codebase that previously had none.