Design a Real-Time Personalized Ad Selection System

Q: Design a Real-Time Personalized Ad Selection System

This question evaluates competency in end-to-end real-time ML system design for personalized ad selection, including model decisioning, feature engineering and stores, exploration–exploitation strategies, offline and online evaluation, and scalability and latency constraints within the Machine Learning domain.

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Question

End-to-End ML System Design: Real-Time Ad Selection

Context

You need to design a real-time, data-driven ad selection system that personalizes ads for millions of users. Assume:

Request volume: millions of users/day; peak QPS in the thousands.
Inventory: tens to hundreds of thousands of ads.
Strict latency targets: p95 < 100 ms for ad decisioning (excluding network).
Business objective: maximize expected value (e.g., clicks or revenue) while respecting advertiser budgets and policies.

Task

Design an end-to-end ML system to select ads at request time. Discuss and justify:

Data collection and logging
Labeling and outcome definition
Feature engineering and a feature store
Model choice and training objectives
Exploration–exploitation strategy (e.g., contextual bandits)
Offline evaluation and counterfactual/replay evaluation
Online evaluation (A/B tests, guardrails)
Latency and reliability constraints
Scalability and architecture (training/serving pipelines)

Hints: CTR prediction with logistic regression or GBDT; counterfactual evaluation (IPS/DR); feature store for train-serve consistency; bandits for exploration.

Design a Real-Time Personalized Ad Selection System

End-to-End ML System Design: Real-Time Ad Selection

Context

Task

Solution

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