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.

Outcome: Shipped a calibrated multiclass NPS model with a utility-driven operating threshold and a PSI-based drift loop, giving the retention team a per-customer detractor probability they can act on and a rule for when to retrain.

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

Outcome: Defined a production calibration gate that logs Brier score, ECE, reliability diagrams, cost-sensitive thresholds, run metadata, and promotion criteria for imbalanced classifiers.

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.

Outcome: Reduced governed dataset onboarding from weeks to days in the sanitized pattern while preserving auditability, cost visibility, and promotion rules for analytics and ML use cases.

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.

Outcome: Added coverage-guaranteed prediction sets and operational abstention gates to a classification pipeline, cutting acted-upon error rate without retraining the model.

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.

Outcome: Identified and resolved training bottlenecks in under an hour by running the three-run baseline and reading profiler signatures before changing any model code.

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

Outcome: Cut training wall-clock time and GPU memory pressure by applying compile, AMP, and accumulation patterns in sequence before ever touching model architecture.