Most ML initiatives stall between “great AUC” and “great business results.” This course closes that gap end to end.

You’ll learn to translate model performance into money by building metric trees that link offline metrics to product KPIs and P&L outcomes. We’ll design defensible measurement plans with the right counterfactuals (A/B, holdouts, geo, diff-in-diff) and guardrails that prevent “wins” that hurt the business elsewhere. You’ll practice power and sample size, variance reduction (CUPED), and lift analysis with confidence intervals. Then we turn lift into ROI: incremental revenue or savings, operating costs, payback and NPV, plus sensitivity analysis to reflect uncertainty. We’ll finish with impact dashboards and an executive narrative that enable clear go/no-go and scale-up decisions. This course is for professionals involved in planning, evaluating, or implementing ML solutions — including Data Scientists, ML Engineers, Business Analysts, Product Managers, and Technology Leaders. It’s also suitable for anyone looking to better connect ML outcomes with business value. Learners should have a basic understanding of Machine Learning concepts and general business workflows, along with an interest in applying data-driven solutions. No advanced coding or mathematics is required. By the end of this course, you’ll consistently connect model metrics to financial outcomes and communicate impact in a way leaders trust—so teams ship fewer models and deliver more value.