ML System Design Interview

ML System Design Interview Framework

Learn how ML system design interviews are scored, a 7-step framework from requirements to monitoring, back-of-envelope estimation for QPS, embeddings and GPU cost, common problem framings, and the mistakes that sink strong candidates.

System Design Basics

Learn the fundamental principles behind building large-scale, reliable systems: scalability, availability, latency, and the key trade-offs that drive real-world architecture decisions.

ML Data Pipelines & Feature Stores

Design the data side of an ML system: logging for train/serve parity, labelling strategies, point-in-time joins, negative sampling, batch vs streaming features, feature stores, training–serving skew, data validation, lineage and a worked notification-click pipeline.

Model Training & Experimentation at Scale

Learn how to answer the training half of an ML system design interview: baselines, model family choice, temporal offline evaluation, tuning budgets, experiment tracking, distributed training, GPU cost estimation, embedding tables, retraining cadence, and off-policy evaluation.

Model Serving & Deployment

Learn to design the serving side of an ML system: batch vs online vs streaming vs hybrid inference, latency budgets with numbers, model server patterns, compression, canary and shadow rollouts, feature-store consistency, autoscaling, and safe fallbacks.

ML Monitoring, Drift & Retraining

Learn why deployed models decay, how to monitor them in layers from system health to business KPIs, compute PSI and other drift statistics with worked numbers, handle delayed labels, and design retraining triggers with safe validation gates and rollback.

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.

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.

Multimodal LLMs and Vision

Learn how multimodal LLMs actually process images: patch tokenization, vision-encoder pretraining, and how the model distinguishes between multiple objects in a scene through attention rather than bounding-box regression. Covers the practical gap between vision-language models and classical object detectors, common failure modes (counting, fine-grained discrimination, spatial relations), and prompting techniques (referring expressions, crops, set-of-mark) that make multi-object questions reliable in production.

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.

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: 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.

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.