Tag: ml

A scikit-learn NPS ordinal classifier with SMOTE, probability calibration, utility-based thresholding, and PSI drift checks. The parts that make it useful to the retention team, not just accurate on a dashboard.

How imbalanced classifiers can keep a strong AUC while producing probabilities that break thresholds, alerts, and cost-sensitive decisions in production.

A sanitized GCP platform case study where compliance, analytics delivery, and ML feature access had to be designed as one release path instead of three disconnected workstreams.

How to add coverage-guaranteed prediction sets, temperature scaling calibration, and risk-coverage curves to a classifier using MAPIE — the pieces that make uncertainty quantification operationally useful rather than decorative.

A repeatable triage routine — the three-run baseline, DataLoader diagnosis, five profiler signatures, and a copy-paste scaffold — for finding where training time actually goes before touching the model.

torch.compile, mixed precision, gradient accumulation, DDP vs FSDP, and the profiler — the five levers I reach for before rethinking the model architecture.