5 Key Questions to Ask Operations Teams Before Building ML Models

A structured framework suggests that data scientists should ask operations teams five critical questions before developing machine learning models. These questions cover when the model will be used in a workflow, what output format is needed, and how frequently predictions must be generated. Understanding the prediction point is essential to prevent data leakage, while knowing the output format helps determine whether the problem requires classification or regression. Clarifying the specific decision the model supports ties the technical work to real business outcomes. Finally, establishing a baseline metric — such as an existing rule-based system — allows teams to measure whether the model delivers genuine improvement.