Olivier Bergeret, Asif Abbasi, Joel Farvault

GenAI on AWS

A Practical Approach to Building Generative AI Applications on AWS

• Broschiertes Buch

Produktbeschreibung

Create cutting-edge generative AI apps on the Amazon Web Services® cloud In Gen AI on AWS®: A Practical Approach to Building Generative AI Applications on AWS, a team of expert cloud and software engineers deliver a hands-on roadmap to creating useful generative AI apps from scratch on the Amazon Web Services cloud platform. You'll find actionable strategies and techniques you can deploy immediately to start writing secure, practical, and reliable applications that implement the latest generative artificial intelligence capabilities. Beginning with novice-friendly introductions to topics like the history of artificial intelligence, the basics of machine learning, and the definition of deep learning, the book goes on to explain exactly how to make use of AWS's extensive library of generative AI tools. Perfect for cloud engineers, cloud solutions consultants, software engineers, and other technical professionals, Gen AI on AWS is also a can't-miss resource for non-technical business leaders, managers, executives, and directors who seek to better understand the opportunities and potential of the AWS-based AI tools already available to firms today.

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Produktdetails

• Verlag: Wiley John + Sons
• Seitenzahl: 352
• Erscheinungstermin: 1. April 2025
• Englisch
• ISBN-13: 9781394281282
• ISBN-10: 1394281285
• Artikelnr.: 70396262

Autorenporträt

OLIVIER BERGERET is a technical leader at Amazon Web Services (AWS), working on database and analytics services. He has over 25 years of experience in data engineering and analytics. Since joining AWS in 2015, he's supported the launch of most of AWS AI services including Amazon SageMaker and AWS DeepRacer. He is a regular speaker and presenter at various data, AI and cloud events such as AWS re:Invent, AWS Summits and third-party conferences. ASIF ABBASI is a Principal Solutions Architect at AWS and has spent the last 20 years working in various roles with focus around Data Analytics, AI/ML, DWH Strategic and Technical Implementations, J2EE Enterprise applications design/development and Project Management. Asif is an Amazon Certified SA, Hortonworks Certified Hadoop professional and Administrator, Certified Spark Developer, SAS Certified Predictive Modeler, along with being a Sun Certified Enterprise Architect and a Teradata Certified Master. JOEL FARVAULT is a Principal Solutions Architect Analytics at Amazon Web Services. He has 25 years' experience working on enterprise architecture, data strategy, and analytics, mainly in the financial services industry. Joel has led data transformation projects on fraud analytics, business intelligence, and data governance. He is also a lecturer on Data Analytics at IA School, at Neoma Business School and at Ecole Superieure de Genie Informatique (ESGI). Joel holds several associate and specialty certifications on AWS.

Inhaltsangabe

Acknowledgments xv
About the Authors xvii
Foreword xix
Introduction xxi
Chapter 1: A Brief History of AI 1
The Precursors of the Mechanical or "Formal" Reasoning 2
The Digital Computer Era 4
Cybernetics and the Beginning of the Robotic Era 6
Birth of AI and Symbolic AI (1955-1985) 10
Subsymbolic AI Era (1985-2010) 14
Deep Learning and LLM (2010-Present) 16
Key Takeaways 17
Chapter 2: Machine Learning 19
What Is Machine Learning? 19
Types of Machine Learning 20
Supervised Learning 21
Unsupervised and Semi-Supervised Learning 22
Reinforcement Learning 23
Methodology for Machine Learning 24
Implementation of Machine Learning 26
Machine Learning Applications 27
Natural Language Processing (NLP) 27
Computer Vision 27
Recommender System 27
Predictive Analytics 28
Fraud Detection 28
Machine Learning Frameworks and Libraries 28
TensorFlow 28
PyTorch 31
Scikit-learn 34
Keras 35
Apache Spark MLlib 37
Future Trends in Machine Learning 40
Rise of Edge Computing and Edge AI 40
Convergence with Emerging Technologies 40
Advancements in Unsupervised Learning,
Reinforcement Learning, and Generative Models 41
Increased Specialization and Customization 41
Explainable and Trustworthy AI 42
Key Takeaways 42
References 43
Chapter 3: Deep Learning 45
Deep Learning vs. Machine Learning 45
Computer Vision Example 46
Natural Language Processing Example 47
The History of Deep Learning 47
Understanding Deep Learning 52
Neurons 52
Weights and Biases 54
Layers 54
Activation Function(s) 55
An Introduction to the Perceptron 58
Overcoming Perceptron Limitations 59
FeedForward Neural Networks 60
Backpropagation 60
Parameters vs. Hyperparameters 60
Hyperparameters in Artificial Neural Networks 64
Loss Functions - a Measure of Success of a Neural Network 64
Optimization Algorithms 64
Neural Network Architectures 68
Putting It All Together 71
Deep Learning on AWS 71
Chipsets and EC2 Instances 71
AWS P5 Instances 72
AWS Inferentia 72
Amazon Elastic Inference 73
Prebuilt Containers: Deep Learning AMIs and Containers 74
Deep Learning AMIs 74
Deep Learning Containers 74
Managed Services for Building, Training, and Deployment 74
Pre-trained Services 75
Key Takeaways 77
References 77
Chapter 4: Introduction to Generative AI 79
Generative AI Core Technologies 80
Neural Networks 80
Generative Adversarial Networks (GANs) 80
Variational Autoencoders (VAEs) 81
Recurrent Neural Networks (RNNs) and
Long Short-Term Memory Networks (LSTMs) 82
Limitations of Recurrent Neural Networks 84
Transformer Models 85
Self-Attention 86
Parallelism 86
Diffusion Models 86
Autoregressive Models 87
Reinforcement Learning (RL) 87
Transfer Learning and Fine-Tuning 87
Optimization Algorithms 87
Transformer Architecture: Deep Dive 87
Deep Dive 89
Step 1: Tokenization (Preprocessing) 89
Step 2: Embedding 89
Step 3: Encoder 92
Step 4: Encoder Output to Decoder Input 97
Step 5: Decoder 98
Step 6: Translation Generation 99
Step 7: Detokenization 99
Terminology in Generative AI 99
Prompt 104
Inference 105
Context Window 106
Prompt Engineering 106
In-Context Learning (ICL) 107
Zero-Shot/One-Shot/Few-Shot Inference 108
Inference Configuration 109
Maximum Length 110
Diversity (Top P/Nucleus Sampling) 111
Top K 111
Randomness (Temperature) 112
System Prompts 112
Prompt Engineering 113
Key Elements of a Prompt 113
Designing Effective Prompts 114
Prompting Techniques 115
Zero-Shot Prompting 115
Few-Shot Prompting 115
Chain-Of-Thought Prompting 116
Advanced Prompting Techniques 117
Self-Consistency 118
Tree of Thoughts (ToT) 119
Retrieval-Augmented Generation (RAG) 120
Automatic Reasoning and Tool-Use (ART) 122
ReAct Prompting 123
Coherence Enhancement 124
Progressive Prompting 126
Handling Prompt Misuse 127
Prompt Injection 127
Prompt Leaking 128
Mitigating Bias 129
Mitigating Bias in Prompt Engineering 130
Generative AI Business Value 133
Building Value Within Your Enterprises 135
Technology: Creating a Flexible and Strong System 135
People: Training and Adapting the Team 135
Processes: Good Management and Fair Use of AI 136
Why a Solid Foundation Is Crucial 136
References 137
Chapter 5: Introduction to Foundation Models 139
Definition and Overview of Foundation Models 139
Characteristics of Foundation Models 142
Examples of Foundation Models 144
Types of Foundation Models 147
The Large Language Model (LLM) 154
Natural Language Processing 155
Early Approaches to NLP 156
Evolution toward Text-Based Foundation Model 160
Applications of Foundation Models 162
Challenges and Considerations 163
Infrastructure 163
Ethics 164
Areas of Evolution 165
Key Takeaways 167
References 168
Chapter 6: Introduction to Amazon SageMaker 169
Data Preparation and Processing 172
Data Preparation 172
Data Processing 173
Model Development 174
Model Training and Tuning 175
Model Deployment 177
Model Management 178
Security 179
Compliance and Governance 180
Model Explainability and Responsible AI 181
MLOps with Amazon SageMaker 181
Boost Your Generative AI Development with
SageMaker JumpStart 182
No-Code ML with Amazon SageMaker Canvas 182
Amazon Bedrock 184
Choosing the Right Strategy for the Development of
Your Generative AI Application with Amazon SageMaker 186
Conclusion 187
References 188
Chapter 7: Generative AI on AWS 191
AWS Services for Generative AI 192
Generative AI Trade-Off Triangle 192
How AWS Solves the Generative AI Trade-Off Triangle 192
Generative AI on AWS: The Fundamentals 193
Infrastructure for FM Training and Inference 194
Models and tools to build Generative AI Apps 194
Applications to boost productivity 195
Amazon Bedrock 196
Foundation Models with Bedrock 197
AI21 Labs - Jurassic 197
Amazon Titan 198
Anthropic's Claude 3 199
Cohere's Family of Models 201
Key Features of Cohere 201
Cohere Models on Amazon Bedrock 203
Meta's Family of Models - Llama 204
When to Use Which Model 207
Mistral's Family of Models 208
When to Use Which Model 209
Stability.ai's Family of Models - Stable Diffusion XL 1.0 209
Poolside Family of Models 210
Luma's Family of Models 211
Amazon's Nova Family of Models 212
Model Evaluation in Amazon Bedrock 213
Common Approaches to Customizing Your FMs 214
Amazon Bedrock Prompt Management 214
Amazon Bedrock Flows 216
Data Automation in Amazon Bedrock 219
GraphRAG in Amazon Bedrock 220
Knowledge Bases in Amazon Bedrock 222
How Knowledge Bases Work 223
Pre-Processing Data 224
Runtime Execution 224
Creating a Knowledge Base in Amazon Bedrock 225
Agents for Amazon Bedrock 225
How Agents Work 226
Components of an Agent at Build Time 226
Components of an Agent at Runtime 228
Guardrails for Amazon Bedrock 230
Security in Amazon Bedrock 231
Amazon Q 232
Amazon Q Business 232
Amazon Q in QuickSight 235
Amazon Q Developer 237
Amazon Q Connect 239
Amazon Q in AWS Supply Chain 240
Summary 241
Chapter 8: Customization of Your Foundation Model 243
Introduction to LLM Customization 244
Continued Pre-Training (Domain Adaptation Fine-Tuning) 244
Fine-Tuning 245
Prompt Engineering 245
Retrieval Augmented Generation (RAG) 246
Choosing Between These Customization Techniques 246
Cost of Customization 249
Customizing Foundation Models with AWS 250
Continuous Pre-Training with Amazon Bedrock 250
Creation of a Training and a Validation Dataset 250
Launch of a Continued Pre-Training Job 251
Analysis of Our Results and Adjustment of
Our Hyperparameters 252
Deployment of Our Model 254
Use Your Customized Model 255
Instruction Fine-Tuning with Amazon Bedrock 257
Instruction Fine-Tuning with Amazon SageMaker JumpStart 257
Conclusion 260
Chapter 9: Retrieval-Augmented Generation 263
What Is RAG? 263
Background and Motivation 264
Overview of RAG 266
Building a RAG Solution 269
Design Considerations 269
Best Practices 270
Common Patterns 271
Performance Optimization 271
Scaling Considerations 272
The Future of RAG Implementations 273
Retrieval Module 274
Retrieval Techniques and Algorithms 276
Augmentation Module 278
Generation Module 280
RAG on AWS 282
Custom Data Pipeline to Build RAG 284
Core Components of a RAG Pipeline 284
Implementation Approaches 286
Basic Solution: LangChain Implementation 286
Advanced Solution: Spark-Based Pipeline 287
Data Ingestion (Examples) 288
Parallel Processing (example) 289
Case Studies and Applications 290
Question-Answering Systems 290
Dialogue Systems 290
Knowledge-Intensive Tasks 291
Implementation Considerations and Best Practices 291
Challenges and Future Directions 292
Example Notebooks 293
References 293
Chapter 10: Generative AI on AWS Labs 295
Lab 1: Introduction to Generative AI with Bedrock 295
Option 1: PartyRock Prompt Engineering Guide
(for Non-Technical and Technical Audiences) 297
Option 2: Amazon Bedrock Labs (for Technical Audiences) 298
Overview of Amazon Bedrock and Streamlit 298
Supported Regions 298
Costs When Running from Your Own Account 298
Quotas When Running from Your Own Account 299
Time to Complete 299
Lab 2: Dive Deep into Gen AI with Amazon Bedrock 299
Lab 3: Building an Agentic LLM Assistant on AWS 300
What Is an Agentic LLM Assistant? 300
Why Build an Agentic LLM Assistant? 301
About This Workshop 301
Architecture 301
Labs 302
Lab 4: Retrieval-Augmented Generation Workshop 303
Managed RAG Workshop 304
Naive RAG Workshop 304
Advance RAG Workshop 304
Audience 304
Lab 5: Amazon Q for Business 304
Next Steps 307
Lab 6: Building a Natural Language Query Engine for Data Lakes 308
Reference 310
Chapter 11: Next Steps 311
The Future of Generative AI: Key Dimensions and
Staying Informed 311
Technical Evolution and Capabilities 312
The Evolution of Scale and Architecture 312
The Multimodal Revolution 312
The Efficiency Breakthrough 313
The Context Window Revolution 313
Real-time Processing and Generation 313
The Future Technological Landscape 314
Application Domains 314
Enterprise Applications: The Quiet Revolution 315
The Scientific Frontier: Accelerating Discovery 315
Healthcare: Personalized Medicine and Diagnosis 315
Education and Training: Personalizing Learning 316
Environmental Applications: Tackling Global Challenges 316
The Future of Applications 317
Ethical and Societal Implications 317
Digital Identity and Deep Fakes: The Crisis of Trust 318
Labor Markets and Economic Disruption 318
Privacy and Data Rights in the Age of AI 318
Bias and Fairness: The Hidden Challenges 319
Democratic Access and Digital Divides 319
Environmental and Sustainability Concerns 319
The Path Forward: Governance and Responsibility 319
Looking to the Future 320
Staying Current in the Rapidly Evolving AI Landscape 320
Glossary 323
Index