- AI工程(影印版)(英文版)
- - 作者：(越)奇普·胡岩|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576620047
  - 出版日期：2025/04/01
  - 页数：509
- 售价：75.6

内容大纲
    基础模型开启了众多全新的AI应用场景，降低了构建AI产品的门槛。这将AI从一门晦涩难懂的学科转变为一种强大的开发工具，即使是没有AI经验的人也能使用。
    在这本通俗易懂的指南中，作者Chip Huyen探讨了AI工程的概念：利用现成的基础模型构建应用的过程。AI应用开发者将学习如何驾驭人工智能领域，包括模型、数据集、评估基准以及看似无穷无尽的应用模式。书中还介绍了一个用于开发AI应用并高效部署的实用框架。
    理解AI工程的概念及其与传统机器学习工程的区别。
    学习AI应用的开发过程，了解每个步骤中的挑战及其解决方法。
    探索各种模型适配技术，包括提示工程、RAG、微调、智能体以及数据集工程，并理解其原理及应用场景。
    分析基础模型在延迟和成本方面的瓶颈，学习克服这些问题的方法。
    根据需求选择合适的模型、评估指标、数据、开发模式。
作者介绍
奇普·胡岩（Chip Huyen）是实时机器学习平台Claypot AI的联合创始人。在NVIDIA、Netflix和Snorkel AI工作期间，她帮助多家大型机构开发和部署了机器学习系统。这本书是基于她在斯坦福大学教授的机器学习系统设计课程（CS 239S）撰写的。
目录
Preface
1. Introduction to Building AI Applications with Foundation Models
  The Rise of AI Engineering
  From Language Models to Large Language Models
  From Large Language Models to Foundation Models
  From Foundation Models to AI Engineering
  Foundation Model Use Cases
    Coding
    Image and Video Production
    Writing
    Education
    Conversational Bots
    Information Aggregation
    Data Organization
    Workflow Automation
    Planning AI Applications
    Use Case Evaluation
    Setting Expectations
    Milestone Planning
    Maintenance
    The AI Engineering Stack
      Three Layers of the AI Stack
      AI Engineering Versus ML Engineering
      AI Engineering Versus Full Stack Engineering
    Summary
2. Understanding Foundation Models
  Training Data
  Multilingual Models
  Domain Specific Models
  Modeling
    Model Architecture
    Model Size
  Post Training
    Supervised Finetuning
    Preference Finetuning
    Sampling
      Sampling Fundamentals
      Sampling Strategies
      Test Time Compute
      Structured Outputs
      The Probabilistic Nature of AI
    Summary
3. Evaluation Methodology
  Challenges of Evaluating Foundation Models
  Understanding Language Modeling Metrics
    Entropy
    Cross Entropy
    Bits per Character and Bits per Byte
    Perplexity
    Perplexity Interpretation and Use Cases

    Exact Evaluation
    Functional Correctness
    Similarity Measures Against Reference Data
    Introduction to Embedding
    AI as a Judge
      Why AI as a Judge?
      How to Use AI as a Judge
      Limitations of AI as a Judge
      What Models Can Act as Judges?
      Ranking Models with Comparative Evaluation
      Challenges of Comparative Evaluation
      The Future of Comparative Evaluation
    Summary
4. Evaluate AI Systems
  Evaluation Criteria
    Domain Specific Capability
    Generation Capability
    Instruction Following Capability
    Cost and Latency
  Model Selection
    Model Selection Workflow
    Model Build Versus Buy
    Navigate Public Benchmarks
    Design Your Evaluation Pipeline
      Step 1. Evaluate All Components in a System
      Step 2. Create an Evaluation Guideline
      Step 3. Define Evaluation Methods and Data
    Summary
5. Prompt Engineering
  Introduction to Prompting
  In Context Learning: Zero Shot and Few Shot
  System Prompt and User Prompt
  Context Length and Context Efficiency
  Prompt Engineering Best Practices
    Write Clear and Explicit Instructions
    Provide Sufficient Context
    Break Complex Tasks into Simpler Subtasks
    Give the Model Time to Think
    Iterate on Your Prompts
    Evaluate Prompt Engineering Tools
    Organize and Version Prompts
    Defensive Prompt Engineering
    Proprietary Prompts and Reverse Prompt Engineering
    Jailbreaking and Prompt Injection
    Information Extraction
    Defenses Against Prompt Attacks
    Summary
6. RAG and Agents
  RAG
    RAG Architecture

    Retrieval Algorithms
    Retrieval Optimization
    RAG Beyond Texts
  Agents
    Agent Overview
    Tools
    Planning
    Agent Failure Modes and Evaluation
    Memory
    Summary
7. Finetuning
  Finetuning Overview
  When to Finetune
  Reasons to Finetune
  Reasons Not to Finetune
  Finetuning and RAG
  Memory Bottlenecks
  Backpropagation and Trainable Parameters
  Memory Math
  Numerical Representations
  Quantization
  Finetuning Techniques
  Parameter Efficient Finetuning
  Model Merging and Multi Task Finetuning
  Finetuning Tactics
  Summary
8. Dataset Engineering
  Data Curation
  Data Quality
  Data Coverage
  Data Quantity
  Data Acquisition and Annotation
  Data Augmentation and Synthesis
    Why Data Synthesis
    Traditional Data Synthesis Techniques
    AI Powered Data Synthesis
  Model Distillation
  Data Processing
    Inspect Data
    Deduplicate Data
    Clean and Filter Data
    Format Data
  Summary
9. Inference Optimization
  Understanding Inference Optimization
  Inference Overview
  Inference Performance Metrics
  AI Accelerators
  Inference Optimization
    Model Optimization

    Inference Service Optimization
  Summary
10. AI Engineering Architecture and User Feedback
  AI Engineering Architecture
    Step 1. Enhance Context
    Step 2. Put in Guardrails
    Step 3. Add Model Router and Gateway
    Step 4. Reduce Latency with Caches
    Step 5. Add Agent Patterns
    Monitoring and Observability
    AI Pipeline Orchestration
  User Feedback
    Extracting Conversational Feedback
    Feedback Design
    Feedback Limitations
  Summary
Epilogue
Index

内容大纲

作者介绍

目录

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