- 机器学习生产系统(影印版)(英文版)
- - 作者：(美)Robert Crowe//Hannes Hapke//Emily Caveness//Di Zhu|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576620054
  - 出版日期：2025/04/01
  - 页数：445
- 售价：66.8

内容大纲
    机器学习（ML）和人工智能（AI）领域正在蓬勃发展，几乎每天都有新的研究、模型和技术出现。面对如此丰富的选择，数据科学家、机器学习工程师和软件开发人员很容易迷失在将AI/ML模型从实验阶段推向生产的众多步骤中。
    这本实用书籍专注于生产环境机器学习，指导你将ML模型转化为可行的产品和应用。生产环境机器学习涵盖了ML的所有领域，不仅限于简单的模型训练。本书特别强调了ML流水线，帮助你为ML生产系统奠定基础。
    你即将开启探索之旅，学习将ML应用投入生产所需的广泛技术，以及需要考虑的问题和方法。关键的ML工程主题包括：
    ·数据收集、验证、存储、特征工程
    ·模型分析、服务、监控、日志记录
    ·使用TensorFlow Extended（TFX）和其他工具编排机器学习流水线
    本书提供了深入的实例，包括适用于自然语言处理（NLP）和计算机视觉模型的端到端机器学习流水线。
作者介绍
目录
Foreword
Preface
1. Introduction to Machine Learning Production Systems
  What Is Production Machine Learning?
  Benefits of Machine Learning Pipelines
  Focus on Developing New Models, Not on Maintaining Existing Models
  Prevention of Bugs
  Creation of Records for Debugging and Reproducing Results
  Standardization
  The Business Case for ML Pipelines
  When to Use Machine Learning Pipelines
  Steps in a Machine Learning Pipeline
  Data Ingestion and Data Versioning
  Data Validation
  Feature Engineering
  Model Training and Model Tuning
  Model Analysis
  Model Deployment
  Looking Ahead
2. Collecting, Labeling, and Validating Data
  Important Considerations in Data Collection
  Responsible Data Collection
  Labeling Data: Data Changes and Drift in Production ML
  Labeling Data: Direct Labeling and Human Labeling
  Validating Data: Detecting Data Issues
  Validating Data: TensorFlow Data Validation
  Skew Detection with TFDV
  Types of Skew
  Example: Spotting Imbalanced Datasets with TensorFlow Data Validation
  Conclusion
3. Feature Engineering and Feature Selection
  Introduction to Feature Engineering
  Preprocessing Operations
  Feature Engineering Techniques
    Normalizing and Standardizing
    Bucketing
    Feature Crosses
    Dimensionality and Embeddings
    Visualization
    Feature Transformation at Scale
    Choose a Framework That Scales Well
    Avoid Training–Serving Skew
    Consider Instance Level Versus Full Pass Transformations
    Using TensorFlow Transform
    Analyzers
    Code Example
  Feature Selection
    Feature Spaces
    Feature Selection Overview
    Filter Methods

    Wrapper Methods
    Embedded Methods
    Feature and Example Selection for LLMs and GenAI
    Example: Using TF Transform to Tokenize Text
    Benefits of Using TF Transform
    Alternatives to TF Transform
    Conclusion
4. Data Journey and Data Storage
  Data Journey
  ML Metadata
  Using a Schema
  Schema Development
  Schema Environments
  Changes Across Datasets
  Enterprise Data Storage
    Feature Stores
    Data Warehouses
    Data Lakes
    Conclusion
5. Advanced Labeling, Augmentation, and Data Preprocessing
  Advanced Labeling
    Semi Supervised Labeling
    Active Learning
    Weak Supervision
    Advanced Labeling Review
  Data Augmentation
    Example: CIFAR 10
    Other Augmentation Techniques
    Data Augmentation Review
  Preprocessing Time Series Data: An Example
    Windowing
    Sampling
    Conclusion
6. Model Resource Management Techniques
  Dimensionality Reduction: Dimensionality Effect on Performance
    Example: Word Embedding Using Keras
    Curse of Dimensionality
    Adding Dimensions Increases Feature Space Volume
    Dimensionality Reduction
  Quantization and Pruning
    Mobile, IoT, Edge, and Similar Use Cases
    Quantization
    Optimizing Your TensorFlow Model with TF Lite
    Optimization Options
    Pruning
  Knowledge Distillation
    Teacher and Student Networks
    Knowledge Distillation Techniques
    TMKD: Distilling Knowledge for a Q&A Task
    Increasing Robustness by Distilling EfficientNets

    Conclusion
7. High-Performance Modeling.
8. Model Analysis.
9. Interpretability
10. Neural Architecture Search
11. Introduction to Model Serving
12. Model Servincl Patterns
13. Model Serving Infrastructure
14. Model Serving Examples
15. Model Manaqement and Delivery
16. Model Monitoring and Logging
17. Privacy and Legal Requirements
18. Orchestrating Machine Learning Pipelines
19. AdvancedTFX
20. ML Pipelines for Computer Vision Problems.
21. ML Pipelines for Natural Language Processing
22. Generative AI
23. The Future of Machine Learning Production Systems and Next Steps
Index

内容大纲

作者介绍

目录

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