Ad Recommender System Design - Grokking the Machine Learning System Design Interview

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Grokking the Machine Learning System Design Interview

1.The Interview Framework and Communication

The 6-Step ML SD Framework Time Management Inside the Interview Diagrams in the ML SD Interview Driving the Interview at Staff+ Level Handling Follow-Up Questions and Pushback Common Failure Modes and Live Recovery Scripts

2.Problem Formulation and Requirements

Why Problem Formulation Is Where Interviews Are Won The Clarifying Questions Playbook Identifying the ML Task Type Functional vs. Non-Functional Requirements Defining Success: Business Metrics vs. ML Metrics Metric Guardrails and Cannibalization Back-of-the-Envelope Estimation for ML Systems

3.Data Strategy: Collection, Pipelines, and Features

Data Sources and Collection Strategies at Scale The Cold Start Problem and Data Scarcity Labeling Strategies and the Active Learning Flywheel Class Imbalance and Data Sampling Feature Types and Engineering Embeddings in ML System Design Feature Pipelines: Batch vs. Real-Time vs. Streaming Feature Stores Data Privacy, Compliance & ML

4.Model Design and Architecture Selection

The Model Selection Framework Classical ML in System Design Neural Architectures for Ranking and Retrieval Neural Architectures for NLP and Vision Graph Neural Networks in System Design Multi-Task and Multi-Objective Learning Loss Functions and Optimization Choices Transfer Learning, Fine-Tuning, and Model Compression Exploration vs. Exploitation

5.Evaluation: Offline, Online, and Fairness

The Two-Phase Evaluation Mindset Offline Metrics by Task Type A/B Testing Design Advanced Experimentation Methods Error Analysis as a Design Skill Slice-Based Evaluation Fairness-Aware Evaluation Evaluation for Generative AI Systems

6.Serving, Deployment, and MLOps

Serving Architectures Retrieval and Ranking Pipelines Low-Latency Serving at Scale Model Optimization for Production LLM Inference Infrastructure ML Deployment Pipelines Feedback Loops and Continual Learning ML Monitoring in Production

7.Case Study: Video Recommendation System

Video Recommendation: Problem Framing & Requirements Video Recommendation: Data Strategy & Feature Engineering Video Recommendation: Candidate Retrieval Architecture Video Recommendation: Ranking & Re-Ranking Video Recommendation: Evaluation, Serving & Trade-Offs

8.Case Study: Social Feed Ranking System

Social Feed Ranking: Problem Framing & Requirements Social Feed Ranking: Data Strategy & Feature Engineering Social Feed Ranking: Model Architecture Social Feed Ranking: Evaluation & Experimentation Social Feed Ranking: Serving, Monitoring & Trade-Offs

9.Case Study: Ad Click-Through Rate Prediction System

Ad CTR Prediction: Problem Framing & Requirements Ad CTR Prediction: Data Strategy & Feature Engineering Ad CTR Prediction: Model Architecture Ad CTR Prediction: Evaluation & Fairness Ad CTR Prediction: Serving & Trade-Offs

Mock Interview

Ad Recommender System Design

10.Case Study: Semantic Search Engine

Semantic Search: Problem Framing & Requirements Semantic Search: Data Strategy & Feature Engineering Semantic Search: Model Architecture Semantic Search: Evaluation & Indexing Semantic Search: Serving & Trade-Offs

11.Case Study: Content Moderation System

Content Moderation: Problem Framing & Requirements Content Moderation: Data Strategy & Multi-Modal Features Content Moderation: Model Architecture Content Moderation: Evaluation & Fairness Content Moderation: Serving & Trade-Offs

Mock Interview

Harmful Content Detection System

12.Case Study: Object Detection System

Object Detection: Problem Framing & Requirements Object Detection: Data Strategy & Augmentation Object Detection: Model Architecture & Compression Object Detection: Evaluation & Safety Design Object Detection: Deployment & Trade-Offs

Mock Interview

Autonomous Vehicle System Design

13.Case Study: Visual Search System

Visual Search: Problem Framing & Requirements Visual Search: Data Strategy & Embedding Generation Visual Search: Model Architecture Visual Search: Serving & Indexing Visual Search: Evaluation & Trade-Offs

Mock Interview

Visual Search System Design

14.Case Study: Fraud Detection System

Fraud Detection: Problem Framing & Requirements Fraud Detection: Data Strategy & Feature Engineering Fraud Detection: Model Architecture Fraud Detection: Evaluation & Adversarial Dynamics Fraud Detection: Serving & Monitoring

Mock Interview

Fraud Detection System

15.Case Study: RAG-Based Enterprise Knowledge Assistant

Enterprise RAG: Problem Framing & Requirements Enterprise RAG: Data Strategy & Retrieval Pipeline Enterprise RAG: Model Architecture & Prompt Design Enterprise RAG: Evaluation Enterprise RAG: Serving & LLM Infrastructure

16.Case Study: LLM-Powered Code Generation Tool

Code Generation: Problem Framing & Requirements Code Generation: Data Strategy & Training Code Generation: Model Architecture Code Generation: Evaluation & Privacy Code Generation: Serving & Trade-Offs