A comprehensive guide to building production-grade sentiment analysis systems that handle millions of user feedback texts daily, covering preprocessing pipelines, model selection, multilingual handling, and closed-loop feedback systems.
A practitioner's guide to A/B testing ML models in production, covering statistical significance, sample size calculations, multi-armed bandits, the tension between model metrics and business KPIs, and the insidious novelty effect.
A practitioner's deep dive into feature engineering for production machine learning systems, covering feature stores, point-in-time correctness, data leakage prevention, automated feature generation, and the critical differences between Kaggle features and production features.
A practitioner's guide to building production MLOps pipelines with Airflow, MLflow, CI/CD for ML, retraining triggers, and monitoring. Based on nine years of deploying and maintaining ML systems in production.
A production-focused technical deep-dive from 9+ years of hands-on data science experience.
A production-focused technical deep-dive from 9+ years of hands-on data science experience.
A comprehensive guide to building production churn prediction systems for telco — CDR feature engineering, survival analysis, uplift modeling, and retention campaign design.
A practitioner's guide to data science in banking and finance — credit scoring, fraud detection, customer segmentation, explainability, imbalanced data, and temporal leakage pitfalls.
A deep dive into collaborative filtering, content-based approaches, Learning to Rank, real-time serving architectures, and A/B testing frameworks for e-commerce recommendation systems.
A practitioner's deep dive into building propensity models using logistic regression and gradient boosting, feature engineering from transaction history, and applying propensity scores to campaign targeting optimization.
A deep technical dive into Learning-to-Rank for e-commerce recommendations, covering pointwise, pairwise, and listwise approaches, feature engineering, serving architectures, A/B testing, and hard-won production lessons from nine years in the trenches.
