內容大鋼
機器學習系統既複雜又獨特。複雜是因為包含大量組件，涉及許多不同的利益方；獨特是因為其依賴於數據，不同用例之間的數據差異很大。在本書中，你將學習以一種整體方法來設計兼具可靠性、可伸縮性、可維護性，並能適應不斷變化的環境和業務需求的機器學習系統。
作者Chip Huyen是CIaypot AI的聯合創始人，她在如何幫助系統作為一個整體實現其目標的背景下考慮了每一種設計決策，例如如何處理和創建訓練數據，使用哪些特性，重新訓練模型的頻率，以及監測哪些內容。書中的迭代框架採用了真實的案例研究，並輔以大量參考資料。
這本書將幫助你處理以下情況：
工程化數據並選擇正確的指標來解決業務問題
實現持續開發、評估、部署和更新模型的流程自動化
開發監控系統，快速檢測和解決模型在生產中可能遇到的問題
構建跨用例服務的機器學習平台
開發可靠的機器學習系統
目錄
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 Environments
From Dev to Prod: Containers
Resource Management
Cron, Schedulers, and Orchestrators
Data Science Workflow Management
ML Platform
Model Deployment
Model Store
Feature Store
Build Versus Buy
Summary
11. The Human Side of Machine Learning
User Experience
Ensuring User Experience Consistency
Combatting "Mostly Correct" Predictions
Smooth Failing
Team Structure
Cross-functional Teams Collaboration
End-to-End Data Scientists
Responsible AI
Irresponsible AI: Case Studies
A Framework for Responsible AI
Summary
Epilogue
Index
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