Part 4 of a 4-part series: From Data to DecisionsMost machine learning projects look successful right up to the moment they are deployed.The notebook runs. The metrics look good. Stakeholders sign off. The system is declared ready.And then reality begi…
Part 3 of a 4-part series: From Data to DecisionsAt some point in most machine learning projects, a familiar moment arrives.The model looks strong. Accuracy is high. ROC curves are smooth. Validation metrics are better than the baseline. Stakeholders a…
Part 2 of a 4-part series: From Data to DecisionsWhy Most Models Are Decided Before Training BeginsMost machine learning projects do not fail because the model was poorly chosen. They fail because the features were easy to compute rather than meaningfu…
Part 1 of a 4-part series: From Data to DecisionsSeries ContextThis is a four-part series built the way real machine learning work happens in banks, not the way it is shown in demos or notebooks.Part 1 focuses on data understanding and exploratory anal…