Lessons From Real ML Deployments

Multi-Stage Pipeline Design

Structuring ML Design Interview Answers

Model Selection for Production Constraints

Handling the Cold-Start Problem

Feedback Loops in Deployed AI

Training vs Serving Path Separation

Estimating Scale in ML Systems

A/B Testing for ML Systems

Online vs Offline Metrics

Data Collection and Labeling Strategy

Two-Tower Retrieval Architecture

Retrieval-then-Ranking Architecture

Fraud Detection System Case Study

Ad Click-Through Rate Prediction

Feed Ranking System Case Study

Search Ranking System Design

Recommendation System Design

ML Requirements: Functional vs Non-Functional

Framing Business Problems as ML

ML System Design Framework