ml-adoption-playbook

Topics in the source

• ML Adoption Playbook
• When to Activate
• Phase 1: Problem Framing & Feasibility
• Phase 2: Data Readiness
• Phase 3: Architectural Integration & Decoupling
• Phase 4: Model Implementation & Training
• Phase 5: Handoff to MLOps
• Iterative Agent Workflow

This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional SWE and MLOps by structuring how ML should be researched, decoupled, trained, and integrated. A user asks to "add ML" or "add an algorithm" to their existing codebase. Planning the integration of a new model (e.g., recommendation, classification, forecasting) into a non-ML application. Structuring a workflow for an agent to build, train, and deploy an ML component adaptively. Before writing model code, establish the "why" and "how". Heuristic Check: Ask the user if a simple heuristic (e.g., regex, rule-based sorting) could solve the problem faster. If yes, start there. Metric Definition: Define what business metric the ML model is trying to improve (e.g., click-through rate, reduced latency). Mistake Budget: Define what a "bad" prediction looks like and how the system should handle it. ML is useless without clean, accessible data. Audit Data Sources: Identify where the training data lives. Is it a live database, a static CSV, or an API? Data Contract: Establish a schema for the input data. What features are required

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