Scratch RecSys 02 - Production-Grade Reference Architecture for ZaPudia
Creating the architecture and tell why need to be that.
• Aryanto
Scratch RecSys 01 - System Design and Scenario Framing for ZaPudia Recommendation Engine
Tracing the architectural evolution from classic ML and collaborative filtering algorithms to sophisticated multi-stage hybrid models, this comprehensive guide focus on System Design, Scenario Framing, Mathematical Foundations.
• Aryanto
Building Recommendation Systems Part 03 - Multi-Model Experimentation and Benchmark Framework
Evaluating AryColBring, Ary2Tower, and LightGBM LTR across 5 ZaPudia Scenarios. Focus on Algorithm Deep-Dive, Hyperparameter Optimization, Offline Benchmarking, Model Selection.
• Aryanto
Building Recommendation Systems Part 04 - Production Inference Architecture and Cost-Optimized MLOps Strategy for ZaPudia
Focus on Hybrid Inference Serving, Cost Optimization, Drift Monitoring, Airflow Orchestration.
• Aryanto
Production Sentiment Analysis at Scale: From Text Preprocessing to Feedback Loops
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.
• Aryanto, M.Si
A/B Testing Machine Learning Models: Statistical Rigor in the Real World
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.
• Aryanto, M.Si
Feature Engineering for Production ML: Feature Stores, Point-in-Time Correctness, and the Kaggle Gap
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.
• Aryanto, M.Si
Designing Production MLOps Pipelines: Orchestration, Versioning, and the Messy Reality
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.
• Aryanto, M.Si
Transfer Learning in Practice: Domain Adaptation, Freezing Strategies, and Hard-Won Lessons
A production-focused technical deep-dive from 9+ years of hands-on data science experience.
• Aryanto, M.Si
ML Monitoring and Drift Detection: Keeping Production Models Honest
A production-focused technical deep-dive from 9+ years of hands-on data science experience.
• Aryanto, M.Si
Data Science in Banking and Finance: Production Lessons from the Trenches
A practitioner's guide to data science in banking and finance — credit scoring, fraud detection, customer segmentation, explainability, imbalanced data, and temporal leakage pitfalls.
• Aryanto, M.Si
Customer Churn Prediction for Telecommunications: CDR Feature Engineering, Survival Analysis, and Retention Campaigns
A comprehensive guide to building production churn prediction systems for telco — CDR feature engineering, survival analysis, uplift modeling, and retention campaign design.
• Aryanto, M.Si
Building Production-Ready Recommendation Engines for E-Commerce Marketing
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.
• Aryanto, M.Si
Propensity Score Modeling for Precision Marketing Campaigns
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.
• Aryanto, M.Si
Building Learning-to-Rank Recommendation Engines for E-Commerce: A Production Playbook
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.
• Aryanto, M.Si