机器学习系统既复杂又独特。复杂是因为包含大量组件，涉及许多不同的利益方；独特是因为其依赖于数据，不同用例之间的数据差异很大。在本书中，你将学习以一种整体方法来设计兼具可靠性、可伸缩性、可维护性，并能适应不断变化的环境和业务需求的机器学习系统。
作者Chip Huyen是CIaypot AI的联合创始人，她在如何帮助系统作为一个整体实现其目标的背景下考虑了每一种设计决策，例如如何处理和创建训练数据，使用哪些特性，重新训练模型的频率，以及监测哪些内容。书中的迭代框架采用了真实的案例研究，并辅以大量参考资料。
这本书将帮助你处理以下情况：
工程化数据并选择正确的指标来解决业务问题
实现持续开发、评估、部署和更新模型的流程自动化
开发监控系统，快速检测和解决模型在生产中可能遇到的问题
构建跨用例服务的机器学习平台
开发可靠的机器学习系统

奇普·胡岩是实时机器学习平台Claypot Al的联合创始人。凭借在NVIDIA、Netflix和Snorkel AI的工作，她帮助了一些世界上最大的组织开发和部署机器学习系统。

Preface
1. Overview of Machine Learning Systems
When to Use Machine Learning
Machine Learning Use Cases
Understanding Machine Learning Systems
Machine Learning in Research Versus in Production
Machine Learning Systems Versus Traditional Software
Summary
2. Introduction to Machine Learning Systems Design
Business and ML Objectives
Requirements for ML Systems
Reliability
Scalability
Maintainability
Adaptability
Iterative Process
Framing ML Problems
Types of ML Tasks
Objective Functions
Mind Versus Data
Summary
3. Data Engineering Fundamentals
Data Sources
Data Formats
ISON
Row-Major Versus Column-Major Format
Text Versus Binary Format
Data Models
Relational Model
NoSQL
Structured Versus Unstructured Data
Data Storage Engines and Processing
Transactional and Analytical Processing
ETL: Extract, Transform, and Load
Modes of Dataflow
Data Passing Through Databases
Data Passing Through Services
Data Passing Through Real-Time Transport
Batch Processing Versus Stream Processing
Summary
4. Training Data
Sampling
Nonprobability Sampling
Simple Random Sampling
Stratified Sampling
Weighted Sampling
Reservoir Sampling
Importance Sampling
Labeling
Hand Labels
Natural Labels
Handling the Lack of Labels
Class Imbalance
Challenges of Class Imbalance
Handling Class Imbalance
Data Augmentation
Simple Label-Preserving Transformations
Perturbation
Data Synthesis
Summary
5. Feature Engineering
Learned Features Versus Engineered Features
Common Feature Engineering Operations
Handling Missing Values
Scaling
Discretization
Encoding Categorical Features
Feature Crossing
Discrete and Continuous Positional Embeddings
Data Leakage
Common Causes for Data Leakage
Detecting Data Leakage
Engineering Good Features
Feature Importance
Feature Generalization
Summary
6. Model Development and 0ffline Evaluation
Model Development and Training
Evaluating ML Models
Ensembles
Experiment Tracking and Versioning
Distributed Training
AutoML
Model Offline Evaluation
Baselines
Evaluation Methods
Summary
7. Model Deployment and Prediction Service
Machine Learning Deployment Myths
Myth 1: You Only Deploy One or Two ML Models at a Time
Myth 2: If We Don't Do Anything, Model Performance Remains the Same
Myth 3: You Won't Need to Update Your Models as Much
Myth 4: Most ML Engineers Don't Need to Worry About Scale
Batch Prediction Versus Online Prediction
From Batch Prediction to Online Prediction
Unifying Batch Pipeline and Streaming Pipeline
Model Compression
Low-Rank Factorization
Knowledge Distillation
Pruning
Quantization
ML on the Cloud and on the Edge
Compiling and Optimizing Models for Edge Devices
ML in Browsers
Summary
8. Data Distribution Shifts and Monitoring
Causes of ML System Failures
Software System Failures
ML-Specific Failures
Data Distribution Shifts
Types of Data Distribution Shifts
General Data Distribution Shifts
Detecting Data Distribution Shifts
Addressing Data Distribution Shifts
Monitoring and Observability
ML-Specific Metrics
Monitoring Toolbox
Observability
Summary
9. Continual Learning and Test in Production
Continual Learning
Stateless Retraining Versus Stateful Training
Why Continual Learning?
Continual Learning Challenges
Four Stages of Continual Learning
How Often to Update Your Models
Test in Production
Shadow Deployment
A/B Testing
Canary Release
Interleaving Experiments
Bandits
Summary
10. Infrastructure and Tooling for MLOps
Storage and Compute
Public Cloud Versus Private Data Centers
Development Environment
Dev Environment Setup
Standardizing Dev Env