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> A voice assistant is not one model. It's a cascade of five distinct ML tasks: binary classification (wake-word), sequence-to-sequence generation (ASR), multi-label classification plus sequence tagging (NLU), policy selection (dialogue management), and conditional audio synthesis (TTS).</blockquote><p>Most candidates treat this as a single "speech-to-response" problem and immediately start talking about end-to-end models. That's a trap. Each stage has its own objective function, latency budget, and failure mode. Your job in the interview is to decompose the pipeline cleanly before proposing any model.</p>

Problem Formulation Clarifying the ML Objective ML framing: A voice assistant is not one model. It's a cascade of five distinct ML tasks: binary…

DataInterview

Design a Voice Assistant

Problem Formulation#

Clarifying the ML Objective#

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Problem Formulation

Clarifying the ML Objective