构建机器学习应用（影印版）

　　通过这本实用的教程，你将学会如何构建一个机器学习模型驱动的应用示例，将最初的想法转化成可部署的产品。数据科学家、软件工程师和产品经理——无论经验丰富的的专家，还是刚刚入门的新手——都可以循序渐进地学习构建真实机器学习应用涉及的工具、最佳实践，完成相关的技术挑战。 学习设计、构建和部署机器学习（ML）应用所需的技能。通过这本实用的教程，你将构建一个机器学习驱动的示例应用程序，将最初的想法转化成可部署的产品。数据科学家、软件工程师和产品经理——无论经验丰富的专家还是刚刚入门的新手——都可以循序渐进地学习构建实际的机器学习应用程序所涉及的工具、最佳实践和技术挑战。

　　Emmanuel Ameisen是Stripe公司的机器学习工程师，曾经为Local Motion和Zipcar公司实施并部署了预测分析和机器学习解决方案。最近，他正在领导洞见数据科学的人工智能项目群，指导着100多个机器学习项目。他拥有法国三所大学的人工智能、计算机工程和管理硕士学位。

Preface
Part I. Find the Correct ML Approach
1. From Product Goal to ML Framing
Estimate What Is sible
Models
Data
Framing the ML Editor
Trying to Do It All with ML: An End-to-End Framework
The Simplest Approach: Being the Algorithm
Middle Ground: Learning from Our Experience
Monica Rogati: How to Choose and Prioritize ML Projects
Conclusion
2. Createa Plan
Measuring Success
Business Performance
Model Performance
Freshness and Distribution Shift
Speed
Estimate Scope and Challenges
Leverage Domain Expertise
Stand on the Shoulders of Giants
ML Editor Planning
Initial Plan for an Editor
Always Start with a Simple Model
To Make Regular Progress: Start Simple
Start with a Simple Pipeline
Pipeline for the ML Editor
Conclusion
Part II. Build a Working Pipeline
3. Build Your First End-to-End Pipeline
The Simplest Scaffolding
Prototype of an ML Editor
Parse and Clean Data
Tokenizing Text
Generating Features
Test Your Workflow
User Experience
Modeling Results
ML Editor Prototype Evaluation
Model
User Experience
Conclusion
4. Acquire an Initial Dataset
Iterate on Datasets
Do Data Science
Explore Your First Dataset
Be Efficient, Start Small
Insights Versus Products
A Data Quality Rubric
Label to Find Data Trends
Summary Statistics
Explore and Label Efficiently
Be the Algorithm
Data Trends
Let Data Inform Features and Models
Build Features Out of Patterns
ML Editor Features
Robert nro: How Do You Find, Label, and Leverage Data?
Conclusion
Part III. Iterate on Models
5. Train and Evaluate Your Model
The Simplest Appropriate Model
Simple Models
From Patterns to Models
Split Your Dataset
ML Editor Data Split
Judge Performance
Evaluate Your Model: Look Beyond Accuracy
Contrast Data and Predictions
Confusion Matrix
ROC Curve
Calibration Curve
Dimensionality Reduction for Errors
The Top-k Method
Other Models
Evaluate Feature Importancek
Directly from a Classifier
Black-Box Explainers
Conclusion
6. Debug Your ML Problems
Software Best Practices
ML-Specific Best Practices
Debug Wiring: Visualizing and Testing
Start with One Example
Test Your ML Code
Debug Training: Make Your Model Learn
Task Difficulty
Optimization Problems
Debug Generalization: Make Your Model Useful
Data Leakage
Overfitting
Consider the Task at Hand
Conclusion
7. Using Classifiers for Writing Recommendations
Extracting Recommendations from Models
What Can We Achieve Without a Model?
Extracting Global Feature Importance
Using a Model's Score
Extracting Local Feature Importance
Comparing Models
Version 1: The Report Card
Version 2: More Powerful, More Unclear
Version 3: Understandable Recommendations
Generating Editing Recommendations
Conclusion
Part IV. Deploy and Monitor
8. Considerations When Deploying Models
Data Concerns
Data Ownership
Data Bias
Systemic Bias
Modeling Concerns
Feedback Loops
Inclusive Model Performance
Considering Context
Adversaries
Abuse Concerns and Dual-Use
Chris Harland: Shipping Experiments
Conclusion
9. Choose Your Deployment Option
Server-Side Deployment
Streaming Application or API
Batch Predictions
Client-Side Deployment
On Device
Browser Side
Federated Learning: A Hybrid Approach
Conclusion
10. Build Safeguards for Models
Engineer Around Failures
Input and Output Checks
Model Failure Fal