ML Tradecraft for T&S

Build T&S ML the way the discipline needs. Learn label sourcing under adversarial drift (where ground truth changes weekly), calibration per surface, slice eval per language and demographic, robustness to evasion (the model-eval-vs-deployed-model gap), the model-versioning discipline, and the analyst-feedback loop that keeps models honest.