The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.

Table of Contents:
Acknowledgments xiii About the Authors xv Foreword xvii Introduction xix 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 62 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 Pre-built 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 136 People: Training and Adapting the Team 136 Processes: Good Management and Fair Use of AI 136 Why a Solid Foundation Is Crucial 136 Summary 137 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 183 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 238 Amazon Q Connect 239 Amazon Q in AWS Supply Chain 241 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) 288 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 Summary 310 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 333