Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments 1835084788, 9781835084786

Table of contents :
Cover
Title page
Copyright and credits
Dedication
Foreword
Contributors
Table of Contents
Preface
Part 1:Integrating Cloud Power with Language Breakthroughs
Chapter 1: Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities
Evolution of conversation AI
What is conversational AI?
Evolution of conversational AI
Introduction to generative AI
The rise of generative AI in 2022-23
Foundation models
LLMs
Core attributes of LLMs
Relationship between generative AI, foundation models, and LLMs
Deep dive – open source vs closed source/proprietary models
Trending models, tasks, and business applications
Text
Image
Audio
Video
Cloud computing for scalability, cost optimization, and security
From vision to value – navigating the journey to production
Summary
References
Chapter 2: NLP Evolution and Transformers: Exploring NLPs and LLMs
NLP evolution and the rise of transformers
The main drawbacks of RNNs and CNNs
NLP and the strengths of generative AI in LLMs
How do transformers work?
Benefits of transformers
Conversation prompts and completions – under the covers
Prompt and completion flow simplified
LLMs landscape, progression, and expansion
Exploring the landscape of transformer architectures
AutoGen
Summary
References
Part 2: Techniques for Tailoring LLMs
Chapter 3: Fine-Tuning – Building Domain-Specific LLM Applications
What is fine-tuning and why does it matter?
Fine-tuning applications
Examining pre-training and fine-tuning processes
Pre-training process
Fine-tuning process
Techniques for fine-tuning models
Full fine-tuning
PEFT
RLHF – aligning models with human values
How to evaluate fine-tuned model performance
Evaluation metrics
Benchmarks
Real-life examples of fine-tuning success
InstructGPT
Summary
References
Chapter 4: RAGs to Riches: Elevating AI with External Data
A deep dive into vector DB essentials
Vectors and vector embeddings
Vector search strategies
When to Use HNSW vs. FAISS
Recommendation System for Articles
Vector stores
What is a vector database?
Vector DB limitations
Vector libraries
Vector DBs vs. traditional databases – Understanding the key differences
Vector DB sample scenario – Music recommendation system using a vector database
Common vector DB applications
The role of vector DBs in retrieval-augmented generation (RAG)
First, the big question – Why?
So, what is RAG, and how does it help LLMs?
The critical role of vector DBs
Business applications of RAG
Chunking strategies
What is chunking?
But why is it needed?
Popular chunking strategies
Chunking considerations
Evaluation of RAG using Azure Prompt Flow
Case study – Global chat application deployment by a multinational organization
Summary
References
Chapter 5: Effective Prompt Engineering Techniques: Unlocking Wisdom Through AI
The essentials of prompt engineering
ChatGPT prompts and completions
Tokens
What is prompt engineering?
Elements of a good prompt design
Prompt parameters
ChatGPT roles
Techniques for effective prompt engineering
N-shot prompting
Chain-of-thought (CoT) prompting
Program-aided language (PAL) models
Prompt engineering best practices
Bonus tips and tricks
Ethical guidelines for prompt engineering
Summary
References
Part 3: Developing, Operationalizing, and Scaling Generative AI Applications
Chapter 6: Developing and Operationalizing LLM-based Apps: Exploring Dev Frameworks and LLMOps
Copilots and agents
Generative AI application development frameworks
Semantic Kernel
LangChain
LlamaIndex
Autonomous agents
Agent collaboration frameworks
AutoGen
TaskWeaver
AutoGPT
LLMOps – Operationalizing LLM apps in production
What is LLMOps?
Why do we need LLMOps?
LLM lifecycle management
Essential components of LLMOps
Benefits of LLMOps
Comparing MLOps and LLMOps
Platform – using Prompt Flow for LLMOps
Putting it all together
LLMOps – case study and best practices
LLMOps field case study
LLMOps best practices
Summary
References
Chapter 7: Deploying ChatGPT in the Cloud: Architecture Design and Scaling Strategies
Understanding limits
Cloud scaling and design patterns
What is scaling?
Understanding TPM, RPM, and PTUs
Scaling Design patterns
Retries with exponential backoff – the scaling special sauce
Rate Limiting Policy in Azure API Management
Monitoring, logging, and HTTP return codes
Monitoring and logging
HTTP return codes
Costs, training and support
Costs
Training
Support
Summary
References
Part 4: Building Safe and Secure AI – Security and Ethical Considerations
Chapter 8: Security and Privacy Considerations for Gen AI – Building Safe and Secure LLMs
Understanding and mitigating security risks in generative AI
Emerging security threats – a look at attack vectors and future challenges
Model denial of service (DoS)
Jailbreaks and prompt injections
Training data poisoning
Insecure plugin (assistant) design
Insecure output handling
Applying security controls in your organization
Content filtering
Managed identities
Key management system
What is privacy?
Privacy in the cloud
Securing data in the generative AI era
Red-teaming, auditing, and reporting
Auditing
Reporting
Summary
References
Chapter 9: Responsible Development of AI Solutions: Building with Integrity and Care
Understanding responsible AI design
What is responsible AI?
Key principles of RAI
Ethical and explainable
Fairness and inclusiveness
Reliability and safety
Transparency
Privacy and security
Accountability
Addressing LLM challenges with RAI principles
Intellectual property issues (Transparency and Accountability)
Hallucinations (Reliability and Safety)
Toxicity (Fairness and Inclusiveness)
Rising Deepfake concern
What is Deepfake?
Some real-world examples of Deepfake
Detrimental effects on society
How to spot a Deepfake
Mitigation strategies
Building applications using a responsible AI-first approach
Ideating/exploration loop
Building/augmenting loop
Operationalizing/deployment loop
Role of AI architects and leadership
AI, the cloud, and the law – understanding compliance and regulations
Compliance considerations
Global and United States AI regulatory landscape
Biden Executive Order on AI
Startup ecosystem in RAI
Summary
References
Part 5: Generative AI – What’s Next?
Chapter 10: The Future of Generative AI – Trends and Emerging Use Cases
The era of multimodal interactions
GPT-4 Turbo Vision and beyond – a closer look at this LMM
Video prompts for video understanding
Video generation models – a far-fetched dream?
Can AI smell?
Industry-specific generative AI apps
The rise of small language models (SLMs)
Integrating generative AI with intelligent edge devices
More important emerging trends and 2024–2025 predictions
From quantum computing to AGI – charting ChatGPT’s future trajectory
What is AGI?
Quantum computing and AI
The impact of AGI on society
Conclusion
References
Index
Other Books You May Enjoy
_Int_V1jQ29D8
_Hlk161251332