ML Adoption Playbook

End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration.

Source: github.com/affaan-m/ECC/tree/main/skills/ml-adoption-playbook · License: MIT