추가 적립 안내

"This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled."

- Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack

"There is so much information one needs to know to be an effective machine learning engineer. It's hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential."

- Laurence Moroney, AI and ML Lead, Google

"One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options."

- Goku Mohandas, Founder of Made With ML

"Chip's manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech―especially those working at 'reasonable scale.' This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild."

- Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU

"Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic."

- Andrey Kurenkov, PhD Candidate at the Stanford AI Lab

Chapter Page
Preface ix
1. Overview of Machine Learning Systems 1
When to Use Machine Learning 3
Machine Learning Use Cases 9
Understanding Machine Learning Systems 12
Machine Learning in Research Versus in Production 12
Machine Learning Systems Versus Traditional Software 22
Summary 23
2. Introduction to Machine Learning Systems Design 25
Business and ML Objectives 26
Requirements for ML Systems 29
Reliability 29
Scalability 30
Maintainability 31
Adaptability 31
Iterative Process 32
Framing ML Problems 35
Types of ML Tasks 36
Objective Functions 40
Mind Versus Data 43
Summary 46
3. Data Engineering Fundamentals 49
Data Sources 50
Data Formats 53
JSON 54
Row-Major Versus Column-Major Format 54
Text Versus Binary Format 57
Data Models 58
Relational Model 59
NoSQL 63
Structured Versus Unstructured Data 66
Data Storage Engines and Processing 67
Transactional and Analytical Processing 67
ETL: Extract, Transform, and Load 70
Modes of Dataflow 72
Data Passing Through Databases 72
Data Passing Through Services 73
Data Passing Through Real-Time Transport 74
Batch Processing Versus Stream Processing 78
Summary 79
4. Training Dat 81
Sampling 82
Nonprobability Sampling 83
Simple Random Sampling 84
Stratified Sampling 84
Weighted Sampling 85
Reservoir Sampling 86
Importance Sampling 87
Labeling 88
Hand Labels 88
Natural Labels 91
Handling the Lack of Labels 94
Class Imbalance 102
Challenges of Glass Imbalance 103
Handling Class Imbalance 105
Data Augmentation 113
Simple Label-Preserving Transformations 114
Perturbation 114
Data Synthesis 116
Summary 118
5. Feature Engineering 119
Learned Features Versus Engineered Features 120
Common Feature Engineering Operations 123
Handling Missing Values 123
Scaling 126
Discretization 128
Encoding Categorical Features 129
Feature Crossing 132
Discrete and Continuous Positional Embeddings 133
Data Leakage 135
Common Causes for Data Leakage 137
Detecting Data Leakage 140
Engineering Good Features 141
Feature Importance 142
Feature Generalization 144
Summary 146
6. Model Development and Offline Evaluation 149
Model Development and Training 150
Evaluating ML Models 150
Ensembles 156
Experiment Tracking and Versioning 162
Distributed Training 168
Auto ML 172
Model Offline Evaluation 178
Baselines 179
Evaluation Methods 181
Summary 188
7. Model Deployment and Prediction Service 191
Machine Learning Deployment Myths 194
Myth 1: You Only Deploy One or Two ML Models at a Time 194
Myth 2: If We Don't Do Anything, Model Performance Remains the Same 195
Myth 3: You Won't Need to Update Your Models as Much 196
Myth 4: Most ML Engineers Don't Need to Worry About Scale 196
Batch Prediction Versus Online Prediction 197
From Batch Prediction to Online Prediction 201
Unifying Batch Pipeline and Streaming Pipeline 203
Model Compression 206
Low-Rank Factorization 206
Knowledge Distillation 208
Pruning 208
Quantization 209
ML on the Cloud and on the Edge 212
Compiling and Optimizing Models for Edge Devices 214
ML in Browsers 222
Summary 223
8. Data Distribution Shifts and Monitoring 225
Causes of ML System Failures 226
Software System Failures 227
ML-Specific Failures 229
Data Distribution Shifts 237
Types of Data Distribution Shifts 237
General Data Distribution Shifts 241
Detecting Data Distribution Shifts 242
Addressing Data Distribution Shifts 248
Monitoring and Observability 250
ML-Specific Metrics 251
Monitoring Toolbox 256
Observability 259
Summary 261
9. Continual Learning and Test in Production 263
Continual Learning 264
Stateless Retraining Versus Stateful Training 265
Why Continual Learning? 268
Continual Learning Challenges 270
Four Stages of Continual Learning 274
How Often to Update Your Models 279
Test in Production 281
Shadow Deployment 282
A/B Testing 283
Canary Release 285
Interleaving Experiments 285
Bandits 287
Summary 291
10. Infrastructure and Tooling for MLOps 293
Storage and Compute 297
Public Cloud Versus Private Data Centers 300
Development Environment 302
Dev Environment Setup 303
Standardizing Dev Environments 306
From Dev to Prod: Containers 308
Resource Management 311
Cron, Schedulers, and Orchestrators 311
Data Science Workflow Management 314
ML Platform 319
Model Deployment 320
Model Store 321
Feature Store 325
Build Versus Buy 327
Summary 329
11. The Human Side of Machine Learning 331
User Experience 331
Ensuring User Experience Consistency 332
Combatting "Mostly Correct" Predictions 332
Smooth Failing 334
Team Structure 334
Cross-functional Teams Collaboration 335
End-to-End Data Scientists 335
Responsible AI 339
Irresponsible AI: Case Studies 341
A Framework for Responsible AI 347
Summary 353
Epilogue 355
Index 357

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.

Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.

This book will help you tackle scenarios such as:

Engineering data and choosing the right metrics to solve a business problem
Automating the process for continually developing, evaluating, deploying, and updating models
Developing a monitoring system to quickly detect and address issues your models might encounter in production
Architecting an ML platform that serves across use cases
Developing responsible ML systems