Subjects

LightGBM

Understand how LightGBM builds sequential ensembles of trees, why leaf-wise growth outperforms level-wise, what every key hyperparameter controls, why a slow learning rate with more trees generalizes better, and how to diagnose and fix overfitting through regularization.

LLM Application System Design

Learn to design LLM-powered systems for interviews and production: prompt vs RAG vs fine-tuning, end-to-end RAG architecture, token and cost budgeting, evaluation, guardrails, agents, caching and routing, with a worked support-assistant design.

Case Study: Video Recommendation (YouTube / Netflix-style Homepage)

Walk through a model ML system design interview answer for a video recommendation homepage: objectives, implicit-feedback labels, two-tower retrieval, multi-task ranking, position bias, cold start, evaluation, A/B testing, serving at scale, and monitoring.

Ranking & Recommendation System Architecture

Learn the multi-stage recommendation architecture: candidate generation with two-tower models and ANN search, pointwise/pairwise/listwise rankers, multi-task value formulas, position debiasing, cold start, re-ranking policy, and how offline metrics relate to online A/B results.

Case Study: Ad Click-Through Rate Prediction (Meta / Google Ads-style)

Model interview answer for ad click-through rate prediction: why calibrated pCTR drives the auction, delayed labels and negative downsampling, sparse ID features, LR-to-DLRM model evolution, calibration monitoring, online learning and a low-latency serving architecture.

Case Study: Search Ranking (Airbnb / E-commerce Marketplace Search)

Model interview answer for marketplace search ranking: two-sided objectives, retrieval vs learned ranking, position-bias-corrected labels, LambdaMART vs neural rankers, NDCG worked example, cold start, diversity re-ranking, serving architecture and monitoring.

Case Study: Real-Time Payment Fraud Detection (Stripe / PayPal-style)

Model interview answer for real-time payment fraud detection: sub-100 ms decisions, 0.1 % positives with asymmetric costs, delayed chargeback labels, point-in-time features and velocity counters, GBDT plus graph signals, PR-AUC and dollar-weighted evaluation, cost-based decision policy and a streaming serving architecture.