本书中的设计模式捕捉了机器学习中反复出现的问题的最佳实践和解决方案。作者是谷歌的三名工程师，他们整理了已证实的方法，帮助数据科学家解决整个ML过程中的常见问题。这些设计模式将数百位专家的经验编纂成直接、平易近人的建议。
在这本书中，你会找到关于数据和问题表示、操作化、可重复性、可再现性、灵活性、可解释性和公平性的30种模式的详细解释。每个模式包括对问题的描述、各种可能的解决方案，以及针对您的情况选择最佳技术的建议。

Preface
1．The Need for Machine Learning Design Patterns
What Are Design Patterns?
How to Use This Book
Machine Learning Terminology
Models and Frameworks
Data and Feature Engineering
The Machine Learning Process
Data and Model Tooling
Roles
Common Chauenges in Machine Learning
Data Quality
Reproducibility
Data Drift
Scale
Multiple Objectives
Summary
2．Data Representation Design Patterns
Simple Data Representations
Numerical Inputs
Categorical Inputs
Design Pattern 1： Hashed Feature
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 2： Embeddings
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 3： Feature Cross
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 4： Multimodallnput
Problem
Solution
Trade-Offs and Alternatives
Summary
3.Problem Representation Design Patterns
Design Pattern 5： Reframing
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 6： Multilabel
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 7： Ensembles
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 8： Cascade
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 9： Neutral Class
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 10： Re alanang
Problem
Solution
Trade-Offs and Alternatives
Summary
4.Model Training Patterns
Typical Training Loop
Stochastic Gradient Descent
Keras Training Loop
Training Design Patterns
Design Pattern 11: Useful Overfitting
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 12: Checkpoints
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 13: Transfer Learning
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 14: Distribution Strategy
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 15: Hyperparameter Tuning
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Summary
5.Design Patterns for Resilient Serving
Design Pattern 16: Stateless Serving Function
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 17: Batch Serving
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 18: Continued Model Evaluation
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 19: Two-Phase Predictions
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 20: Keyed Predictions
Problem
Solution
Trade-Offs and Alternatives
Summary
6.Reproducibility Design Patterns
Design Pattern 21: Transform
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 22: Repeatable Splitting
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 23: Bridged Schema
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 24: Windowed Inference
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 25: Workflow Pipeline
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 26: Feature Store
Problem
Solution
Why It Works
Trade-Offs and Alternatives
Design Pattern 27: Model Versioning
Problem
Solution
Trade-Offs and Alternatives
Summary
7.Responsible AI
Design Pattern 28: Heuristic Benchmark
Problem
Solution
Trade-Offs and Alternatives
Design Pattern 29: Explainable Predictions
Problem
Solution
Trade-Offs and