- 可靠的机器学习(影印版)(英文版)
- - 作者：(美国)凯茜·陈//(爱尔兰)尼尔·理查德·墨菲//(美国)克拉蒂·帕里萨//D.斯卡利//托德·安德伍德|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576605525
  - 出版日期：2023/03/01
  - 页数：376
- 售价：47.6

内容大纲
    无论你是小型创业公司还是跨国公司的一员，这本实践用书都为你(数据科学家、软件和网站可靠性工程师、产品经理或企业主)展示了如何在组织内可靠、有效和负责地运行和建立机器学习。你将深入了解其中涉及的方方面面，从如何在生产中进行模型监控到如何在产品组织中运营一个完善的模型开发团队。
    通过将SRE思维应用于机器学习，作为本书作者和工程专业人士的Cathy Chen、Kranti Parisa、Niall Richard Murphy、D.Sculley、Todd Underwood以及特邀作者向你展示了如何运行高效可靠的机器学习系统。无论你是想增加收入、优化决策、解决问题，还是想理解和影响客户行为，你都将学到如何执行日常的机器学习任务，同时保持更广阔的视野。
    本书内容包括：
    ·什么是ML：运作方式以及依赖什么
    ·用于理解机器学习“环路”如何工作的概念框架
    ·有效的生产如何使机器学习系统易于监控、部署和操作
    ·为什么机器学习系统使生产故障排除更加困难，以及如何进行相应的补偿
    ·机器学习、产品和生产团队如何有效沟通
作者介绍
目录
Foreword
Preface
1. Introduction
  The ML Lifecycle
    Data Collection and Analysis
    ML Training Pipelines
    Build and Validate Applications
    Quality and Performance Evaluation
    Defining and Measuring SLOs
    Launch
    Monitoring and Feedback Loops
   Lessons from the Loop
2. Data Management Principles
  Data as Liability
  The Data Sensitivity of ML Pipelines
  Phases of Data
    Creation
    Ingestion
    Processing
    Storage
    Management
    Analysis and Visualization
  Data Reliability
    Durability
    Consistency
    Version Control
    Performance
    Availability
  Data Integrity
    Security
    Privacy
    Policy and Compliance
  Conclusion
3. Basic Introduction to Models
  What Is a Model?
  A Basic Model Creation Work_flow
  Model Architecture Versus Model Definition Versus Trained Model
  Where Are the Vulnerabilities?
    Training Data
    Labels
    Training Methods
  Infrastructure and Pipelines
    Platforms
    Feature Generation
    Upgrades and Fixes
  A Set of Useful Questions to Ask About Any Model
  An Example ML System
    Yarn Product Click-Prediction Model
    Features
    Labels for Features

    Model Updating
    Model Serving
    Common Failures
  Conclusion
4. Feature and Training Data
  Features
    Feature Selection and Engineering
    Lifecycle of a Feature
    Feature Systems
  Labels
  Human-Generated Labels
    Annotation Workforces
    Measuring Human Annotation Quality
  ……
5. Evaluating Model Validity and Quality
6. Fairness, Privacy, and Ethical ML Systems
7. Training Systems
8. Serving
9. Monitoring and Observability for Models
10. Continuous ML
11. Incident Response
12. How Product and ML Interact
13. Integrating ML into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps in Practice
Index

内容大纲

作者介绍

目录

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