간편결제, 신용카드 청구할인
인터파크 롯데카드 5% (78,330원)
(최대할인 10만원 / 전월실적 40만원)
북피니언 롯데카드 30% (57,720원)
(최대할인 3만원 / 3만원 이상 결제)
NH쇼핑&인터파크카드 20% (65,960원)
(최대할인 4만원 / 2만원 이상 결제)

Designing Machine Learning Systems : An Iterative Process for Production-Ready Applications


2013년 9월 9일 이후 누적수치입니다.



  • 82,450 (3%할인)

    2,480P (3%적립)

  • S-Point 적립은 마이페이지에서 직접 구매확정하신 경우만 적립 됩니다.
  • 6/17(월) 이내 발송 예정  (서울시 강남구 삼성로 512)
  • 무료배송
감소 증가
  • 이벤트/기획전

  • 연관도서(1)

  • 상품권


출판사 서평

"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
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


Huyen, Chip [저] 신작알림 SMS신청
생년월일 -

해당작가에 대한 소개가 없습니다.

컴퓨터 분야에서 많은 회원이 구매한 책


    0.0 (총 0건)


    작성시 유의사항



    (총 0건)


    • 인터파크도서에 등록된 오픈마켓 상품은 그 내용과 책임이 모두 판매자에게 있으며, 인터파크도서는 해당 상품과 내용에 대해 책임지지 않습니다.














    서울특별시 종로구 종로 1(종로1가,교보빌딩)


    반품/교환 방법

    ‘마이페이지 > 취소/반품/교환/환불’ 에서 신청 또는 1:1 문의 게시판 및 고객센터(1577-2555)에서 신청 가능

    반품/교환가능 기간

    변심 반품의 경우 출고완료 후 6일(영업일 기준) 이내까지만 가능
    단, 상품의 결함 및 계약내용과 다를 경우 문제점 발견 후 30일 이내

    반품/교환 비용

    변심 혹은 구매착오로 인한 반품/교환은 반송료 고객 부담
    상품이나 서비스 자체의 하자로 인한 교환/반품은 반송료 판매자 부담

    반품/교환 불가 사유

    ·소비자의 책임 있는 사유로 상품 등이 손실 또는 훼손된 경우
    (단지 확인을 위한 포장 훼손은 제외)

    ·소비자의 사용, 포장 개봉에 의해 상품 등의 가치가 현저히 감소한 경우
    예) 화장품, 식품, 가전제품(악세서리 포함) 등

    ·복제가 가능한 상품 등의 포장을 훼손한 경우
    예) 음반/DVD/비디오, 소프트웨어, 만화책, 잡지, 영상 화보집

    ·시간의 경과에 의해 재판매가 곤란한 정도로 가치가 현저히 감소한 경우

    ·전자상거래 등에서의 소비자보호에 관한 법률이 정하는 소비자 청약철회 제한 내용에 해당되는 경우

    상품 품절

    공급사(출판사) 재고 사정에 의해 품절/지연될 수 있음

    소비자 피해보상
    환불지연에 따른 배상

    ·상품의 불량에 의한 교환, A/S, 환불, 품질보증 및 피해보상 등에 관한 사항은 소비자분쟁해결 기준 (공정거래위원회 고시)에 준하여 처리됨

    ·대금 환불 및 환불지연에 따른 배상금 지급 조건, 절차 등은 전자상거래 등에서의 소비자 보호에 관한 법률에 따라 처리함

    (주)KG이니시스 구매안전서비스서비스 가입사실 확인

    (주)인터파크커머스는 회원님들의 안전거래를 위해 구매금액, 결제수단에 상관없이 (주)인터파크커머스를 통한 모든 거래에 대하여
    (주)KG이니시스가 제공하는 구매안전서비스를 적용하고 있습니다.


    • 교보문고 상품은 택배로 배송되며, 출고완료 1~2일내 상품을 받아 보실 수 있습니다.

    • 출고가능 시간이 서로 다른 상품을 함께 주문할 경우 출고가능 시간이 가장 긴 상품을 기준으로 배송됩니다.

    • 군부대, 교도소 등 특정기관은 우체국 택배만 배송가능합니다.

    • 배송비는 업체 배송비 정책에 따릅니다.

    • - 도서 구매 시 15,000원 이상 무료배송, 15,000원 미만 2,500원 - 상품별 배송비가 있는 경우, 상품별 배송비 정책 적용