Pragmatic ML Guide: From Business Case Definition to Production Rollback

A practical machine learning framework emphasizes that model development should begin with a clearly defined financial or operational goal, not just a target accuracy metric. Practitioners are advised to establish a historical baseline and start with simple models like linear regression before considering complex alternatives, adding complexity only when justified by business requirements. Interpretability is prioritized, with tools like SHAP or LIME recommended when black-box models are unavoidable, since unexplainable predictions pose deployment risks. Evaluation must go beyond average error to stress-test models against data drift, edge cases, and corrupted inputs, while comparing maintenance costs against actual business value. In production, containerization, version control, rollback procedures, and thorough documentation — including a monitoring playbook — are outlined as essential safeguards for sustainable ML operations.