The ML Playbook

The ML System Design Interview Framework

ML Estimation & Capacity Planning

Choosing the Right ML Architecture

ML Concepts

Feature Stores

Model Serving & Inference

Training Pipelines

ML Monitoring & Observability

Embedding Systems

Feature Engineering at Scale

Experiment Platforms for ML

Data Labeling & Annotation Pipelines

Model Versioning & Registry

Retrieval-Augmented Generation (RAG)

Online Learning & Continual Training

GPU Infrastructure & Scheduling

ML Design Cases

Design a Recommendation System

Design a Search Ranking System

Design a News Feed Ranking System

Design an Ads Click Prediction System

Design a Fraud Detection System

Design an Image Search System

Design an AutoML Platform

Design a Real-Time Personalization Engine

Design a Content Moderation System

Design an ETA Prediction System

Design a Conversational AI System

Design a Dynamic Pricing System

Design a Similar Items System

Design a Voice Assistant

Design Product Recommendation on Amazon

Requirement Gathering

Architecture Design

Data Preparation

Model Training

Model Serving

Predict Trip ETA on Uber

Pay Range on Glassdoor

Design ChatGPT

Machine Learning System Design

<h2 id="problem-formulation">Problem Formulation</h2><h3 id="clarifying-the-ml-objective">Clarifying the ML Objective</h3><blockquote><strong>ML framing:</strong> Given raw data and a declared task type, the platform must automatically produce a trained, validated model that optimizes a user-specified metric (e.g., AUC for classification, RMSE for regression) and deploy it as a callable endpoint.</blockquote>

Problem Formulation Clarifying the ML Objective ML framing: Given raw data and a declared task type, the platform must automatically produce a trained,…

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Design an AutoML Platform

Problem Formulation#

Clarifying the ML Objective#

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Problem Formulation

Clarifying the ML Objective