How We Ensure Model Quality
Our process focuses on precision, explainability, and performance – ensuring every deployed ML model adds measurable value.
Coding & Data Practices
Follow clean code, reproducibility, and feature documentation.
Model Evaluation
Use cross-validation, confusion matrices, and ROC-AUC metrics for assessment.
Peer Review & Validation
Combine automated tests and manual validation to reduce bias and data leakage.
Model Monitoring
Track drift, retraining frequency, and live accuracy for consistent performance.
