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Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key Features Gain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloud Understand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AI Peek into the future to explore emerging trends like multimodal AI and autonomous agents Purchase of the print or Kindle book includes a free PDF eBook Book Description Generative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms. This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you'll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you'll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements. By the end of this book, you'll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services. What you will learn Get started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function together Understand how we started applying NLP to concepts like transformers Grasp the process of fine-tuning and developing apps based on RAG Explore effective prompt engineering strategies Acquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIs Discover how to scale and secure GenAI systems, while understanding the principles of responsible AI Who this book is for This artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book. Table of Contents Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities NLP Evolution and Transformers: Exploring NLPs and LLMs Fine Tuning: Building Domain Specific LLM Applications RAGs to Riches: Elevating AI with External Data Effective Prompt Engineering Strategies: Unlocking Wisdom Through AI Developing and Operationalizing LLM-Based Cloud Applications: Exploring Dev Frameworks and LLMOps Deploying ChatGPT in the Cloud: Architecture Design and Scaling Strategies Security and Privacy Considerations for Gen AI: Building Safe and Secure LLMs Responsible Development of AI Solutions: Building with Integrity and Care Future of Generative AI: Trends and Emerging Use Cases 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 Master ChatGPT, Transform Data Security, and Innovate Responsibly to Shape the Future of AI Discover the transformative power of AI technologies like ChatGPT in this era of human-machine interaction. 'ChatGPT for Cloud Solutions' is your comprehensive guide to fully harnessing the potential of this cutting-edge technology on cloud platforms. From the foundations of cloud computing and ChatGPT to security considerations, responsible AI principles, and the innovative concept of Prompt Engineering, this book covers it all. You'll learn how to architect and deploy highly scalable and secure ChatGPT solutions on the cloud, and master the art of creating conversational AI, including chatbots, using this remarkable technology. Fine-tune your AI applications to cater to your specific needs and explore captivating case studies from diverse industries, showcasing the transformative impact of ChatGPT. By the end of this book, you'll have a firm grasp of ChatGPT's features and cloud computing, empowering you to develop innovative, efficient, and ethical AI solutions across a multitude of applications. The audience for this book is primarily focused on Cloud Architects, Data Analysts, Data Scientists, and AI Architects/AI Managers/AI Researchers, however it may also appeal to anyone interested in the quickly growing technology between ChatGPT, LLMs, and cloud computing. The level of understanding of the audience can be from beginner to intermediate - readers with some experience and knowledge in AI is helpful, however not required.