06607nam a22004693i 4500 000724873 MiAaPQ 20250614100445.0 m o d | cr cnu|||||||| 250614s2025 xx o ||||0 eng d NEZPRACOVANÝ IMPORT 9781098166274 (electronic bk.) 9781098166304 (MiAaPQ)EBC31813154 (Au-PeEL)EBL31813154 (OCoLC)1477220546 Huyen, Chip. AI Engineering : Building Applications with Foundation Models. 1st ed. Sebastopol : O'Reilly Media, Incorporated, 2025. ©2025. 1 online resource (535 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Cover -- Copyright -- Table of Contents -- Preface -- What This Book Is About -- What This Book Is Not -- Who This Book Is For -- Navigating This Book -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 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 -- Chapter 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 -- Chapter 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 Measurements 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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. Chapter 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 -- Chapter 9. Inference Optimization -- Understanding Inference Optimization -- Inference Overview -- Inference Performance Metrics -- AI Accelerators -- Inference Optimization -- Model Optimization -- Inference Service Optimization -- Summary -- Chapter 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 -- About the Author -- Colophon. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. elektronické knihy fd186907 czenas electronic books eczenas Print version: Huyen, Chip AI Engineering Sebastopol : O'Reilly Media, Incorporated,c2025 9781098166304 ProQuest (firma) ko2008435111 https://ebookcentral.proquest.com/lib/vsep/detail.action?docID=31813154 Plný text https://knihovna.vse.cz/zdroje/proquest-ebook-central/?info Informace o práci s e-knihami PQ-OWNED EB NEPOSILAT EB