GenAI & Agentic AI by BPB

DESCRIPTION This book explores how generative AI is transforming the healthcare industry, enabling smarter decision-making, enhanced patient care, and improved operational efficiency. This book serves as your essential guide to understanding and applying these cutting-edge technologies, specifically designed for healthcare professionals seeking to navigate this evolving landscape. You will go through the core principles of generative AI, starting with foundational deep learning and neural network concepts and then progressing to practical applications for payers, providers, and health tech innovators. Readers will understand how payers can leverage AI, while providers will learn how AI can enhance medical imaging, generate synthetic patient data, and enable personalized treatment planning. Life sciences companies will discover AI’s role in accelerating drug discovery, powering diagnostics, and creating personalized health applications. This book addresses vital aspects of data management, including privacy and ethical considerations. Finally, you will look into future trends, such as AI-assisted surgery and personalized medicine, and learn the collaborative strategies necessary to integrate generative AI into modern healthcare. By the end of this book, you will have understood how generative AI functions and how it can be applied across the healthcare sector, empowering you to confidently contribute to and lead the next wave of innovation in patient care and medical technology. WHAT YOU WILL LEARN ● Understand generative AI fundamentals and its role in healthcare. ● Apply AI for fraud detection, risk assessment, and efficiency. ● Leverage AI to enhance imaging, diagnostics, and patient care. ● Navigate data challenges, compliance, and ethical AI concerns. ● Implement AI strategies for payers, providers, and life sciences. ● Explore future AI trends and innovations in healthcare. ● Build AI wellness apps, understand HIPAA/GDPR, and prepare for AI's future. WHO THIS BOOK IS FOR This book is for healthcare professionals, technology leaders, and business executives looking to understand and implement generative AI in healthcare. A basic understanding of healthcare operations, data management, or AI concepts is helpful but not required. TABLE OF CONTENTS 1. Introduction to Generative AI in Healthcare 2. Generative AI for Healthcare Professionals 3. Generative AI for Payers 4. Generative AI for Providers 5. Generative AI for Health Tech Companies 6. Data Considerations for Generative AI in Healthcare 7. Ethical and Regulatory Considerations 8. Future Outlook and Opportunities 9. Towards a Generative AI-Driven Healthcare

Understand how to leverage Hyperautomation and Generative AI to accelerate Business Transformation KEY FEATURES ● Explores a wide range of use cases and examples that demonstrate the diverse applications of Hyperautomation across industries, sectors, and specific departments. ● Familiarizes you with popular tools and platforms like UiPath, Automation Anywhere, and IBM. ● Discovers how existing organizations utilizing AI and RPA technologies can leverage Hyper automation to rapidly expand automation initiatives across various business verticals. ● Rise of Generative AI and its Tectonic Shift in industries and technologies. DESCRIPTION Hyperautomation and Generative AI, when combined, can create powerful new capabilities for businesses. If you are a business leader looking to improve your productivity, efficiency, and customer satisfaction by adopting hyperautomation and generative AI technologies, then this book is for you. This book provides an introduction to hyperautomation, highlighting its key components and providing guidance on how organizations can implement it to streamline everyday business operations. The book covers a comprehensive range of use cases and examples that demonstrate the diverse applications of hyperautomation across industries, sectors, and specific departments within companies. It also familiarizes you with popular tools and platforms like UiPath, Automation Anywhere, and IBM, enabling them to make informed decisions when selecting the appropriate technology for their digital transformation endeavors. Lastly, the book illustrates how existing organizations that are already utilizing AI and RPA technologies can leverage hyperautomation to rapidly expand their automation initiatives throughout various business verticals. By the end of the book, you will have a deep understanding of the potential of hyperautomation and generative AI to transform businesses. WHAT YOU WILL LEARN ● Understand the importance of developing the necessary talent, skillset, and IT infrastructure for successful Hyperautomation implementation. ● Explore different strategies for scaling automation initiatives across different business verticals. ● Understand how to solve diverse business problems using Hyperautomation through a high-level plan. ● Understanding the essential technologies like AI, machine learning, and deep learning and their contributions to Hyperautomation. ● Understand the benefits of using Hyperautomation with Generative AI. WHO THIS BOOK IS FOR This book is for professionals who are interested in automation and Generative AI. It covers the development and execution of diverse business processes, and is relevant to a wide range of roles, including digital transformation consultants, CxOs, technical architects, AI engineers, enterprise architects, RPA engineers, and automation engineers. TABLE OF CONTENTS Section I: Automation and Its Necessity 1. The Realism of Hyperautomation 2. Existence of Different Automations 3. Fundamentals of RPA Tools and Platforms 4. Amalgam of Hyperautomation and RPA Section II : Evolution of Automation to Hyperautomation via RPA 5. Devising Hyperautomation Solutions 6. Amalgam of Hyperautomation and Artificial Intelligence 7. Bridging AI with Humans 8. Impact of Machine Learning with Hyperautomation 9. Operationalizing Hyperautomation 10. Successful Use Cases of Hyperautomation Section III: Emergence of Generative AI and Its Collaboration with Hyperautomation 11. Generative AI and Hyperautomation

DESCRIPTION Generative AI for Financial Services is a book targeted at any AI practitioner who wants to understand how generative AI works on a fundamental level and extend that learning to risks, challenges, and opportunities in regulated industries like financial services. This book explores generative AI and its applications in financial services. It starts by explaining key concepts, differences from traditional AI, and the generative AI lifecycle. Readers learn about foundation models, transfer learning, and implementation strategies, including fine-tuning and training models. Practical use cases like improving customer experiences, automating ESG analysis, and optimizing contact centers are covered. The book also addresses challenges like model bias, ethics, and compliance while focusing on building trustworthy AI with fairness and accountability. Finally, it highlights MLOps and FMOps for efficient AI model deployment and management in production environments. By the end of this book, you will be well-equipped to navigate the exciting world of generative AI and confidently apply its transformative potential within the financial services landscape. KEY FEATURES ● Evolve from a generative AI enthusiast to an advanced practitioner in generative AI. ● Learn effective ways of building generative AI solutions for financial services. ● Become familiar with the upcoming trends in generative AI space. WHAT YOU WILL LEARN ● Learn about popular use-cases, challenges, risks in financial services, and how to overcome those. ● Learn how to launch and scale generative AI applications in regulated environments like financial services, expanding from tens to thousands of use cases. ● Understand the operational aspects of AI, including MLOps and FMOps. ● Address challenges and ethical considerations in AI implementation. WHO THIS BOOK IS FOR This book is suitable for generative AI engineers, prompt engineers, ML engineers, data scientists, AI/ML, and technical leaders in financial services. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Types of Foundation Models 3. Generative AI Implementation Approaches 4. Generative AI Use-cases in Financial Services 5. Generative AI Challenges for Financial Services 6. Generative AI Anti-Patterns for Financial Services 7. Architectural Considerations in Generative AI 8. Use-case: Improving Customer Experience with Generative AI 9. Use-case: Contact Center Insights with Generative AI 10. Use-case: Fine-tuning for Financial Services 11. Use case: Automating ESG Analysis Using Agents 12. Choosing the Right Implementation Approach 13. Increasing Trustworthiness in Generative AI 14. Building Foundation Model Operations Practices

DESCRIPTION AI is redefining how businesses solve problems, automate decisions, and deliver value. LangChain, an emerging framework, streamlines the integration of large language models into real-world applications, making AI more actionable and accessible. Unlock the power of cutting-edge AI with LangChain, a revolutionary Python framework designed to simplify the creation of sophisticated AI applications. This book begins with the foundational knowledge of AI and LangChain, followed by practical tools like Python, neural networks, and cloud platforms such as AWS and Azure. You will explore LangChain’s architecture and gain hands-on experience integrating LangChain with leading cloud platforms. You will discover how to leverage powerful data science tools like Snowflake and Athena for real-world AI projects. You will analyze AI applications in modern operational frameworks, such as DevOps and MLOps, and gain insights into the exciting future trends, ethical AI practices, and real-world deployment strategies that are shaping the evolution of AI and LangChain. By the end of this book, readers will confidently design, develop, and deploy intelligent AI applications using LangChain. You will be equipped to solve real-world problems through scalable, cloud-integrated AI systems with strong coding, deployment, and data-handling skills. WHAT YOU WILL LEARN ● Understand AI fundamentals, LangChain's architecture, ethical AI, and Python setup. ● Master Python basics, control structures, functions, and modules for AI development. ● Explore LangChain's core components, workflow, and practical implementation in AI. ● Apply AI, including LangChain, in DevOps and MLOps for automation and efficiency. ● Explore future AI trends, LangChain's evolving role, and ethical considerations. ● Integrate LangChain with AWS services for scalable AI deployment and management. ● Utilize Snowflake and Athena with LangChain for real-world data-driven AI projects. WHO THIS BOOK IS FOR This book is ideal for software developers, AI enthusiasts, data scientists, DevOps engineers, and tech professionals aiming to gain practical skills in AI implementation using LangChain. Educators and students in computer science will also benefit from its hands-on guidance. TABLE OF CONTENTS 1. Introduction to Artificial Intelligence and LangChain 2. Getting Started with Python 3. Understanding LangChain Basics 4. Neural Network with LangChain 5. LangChain and AWS Integration 6. LangChain and Azure Integration 7. Real-world Data Science with Snowflake and Athena 8. AI in DevOps and MLOps 9. Future Trends in AI and LangChain APPENDIX 1: Additional Resources and References APPENDIX 2: Glossary of Terms

DESCRIPTION Generative AI can streamline technical and business processes, increase efficiency, and free up your resources’ time to focus on more strategic initiatives. This book takes the readers through a series of steps to deepen their understanding of the forces that shape an organization’s implementation of Generative AI at scale and successfully dealing with them. This book starts with GenAI potential uses, challenges and enterprise deployment strategies. You will learn to scale GenAI models along with LLMOps, choose the right LLM, and use prompt engineering and fine-tuning to customize the outputs. This book introduces a GenAI operating system as well as an orchestration platform for workflow automation. It discusses ethical considerations, designing a target operating model, cost optimization, Retrieval-augmented Generation (RAG), Model as a Service (MaaS), and Confidential AI. Finally, it explores the future of multi-modal AI assistants in enterprises. This book makes it easier for readers to debunk myths, and address fallacies and common misconceptions that could harm organizational investment and reputation. There are also practical and enterprise class scenarios and information that could help in improving implementations, within your organization, enabling you to achieve success beyond scaling challenges. KEY FEATURES ● Understand challenges and dimensions of model at scale. ● Understand model selection criteria, deployment patterns, and positioning. ● Design operating system and demarcation of landing zones. ● Understand enterprise application of prompt engineering and fine-tuning. ● Understand operating model, orchestration platform, multi AI assistants and ethical considerations. ● Understand various latency factors for Gen AI solutions. WHAT YOU WILL LEARN ● Strategies for scaling GenAI models and discovering LLMOps for managing them. ● How to leverage GenAI to streamline enterprise class processes, boost efficiency, and explore new possibilities. ● Implementations in the enterprise class deployments, addressing potential issues and connecting with enablers and accurate growth strategy and execution principles. WHO THIS BOOK IS FOR This book is for decision makers like CIOs, CTOs, CAIOs, Enterprise Architects, Chief Engineers, and anyone who wishes to learn how to have a rewarding implementation of Generative AI for their organizations and clients. TABLE OF CONTENTS 1. The Rise of Generative AI in Enterprises 2. Complex Needs of Production 3. Model Selection for Enterprises 4. Model Deployment for Enterprises 5. Operating System for Enterprises 6. Prompt Engineering for Enterprises 7. Fine-tuning for Enterprises 8. Orchestration of Generative AI Workflows 9. Six Ethical Dimensions for Enterprises 10. Designing a Target Operating Model 11. Cost Optimization Strategies 12. Retrieval-augmented Generation for Enterprises 13. Model as a Service for Enterprises 14. Confidential AI 15. Latency in Generative AI Solutions 16. Multi-modal Multi-agentic Assistant Framework for Enterprises

DESCRIPTION Generative AI is changing the way we think about creativity and problem-solving. This book is your go-to guide for understanding and working with this exciting technology. This book offers a clear introduction to generative AI, starting with basics like machine learning and deep learning. It explains key models, including GANs and VAEs, breaking down their architectures and training methods. You will discover how Transformer models like GPT have transformed natural language processing and enabled advancements in language generation. The book explores practical applications such as image synthesis, style transfer, and text generation, showing how generative AI merges technology with creativity. Advanced topics like reinforcement learning, AI ethics, and bias are also covered. Practical tips for creating your own generative AI models, along with insights into the future of this groundbreaking field, making it an essential resource for AI enthusiasts and professionals. By the end of this book, you will have a firm grasp of generative AI concepts and practical skills to get you started. You will be well-prepared to use cloud platforms like AWS, Azure, and GCP to build and launch powerful generative AI projects. From creating realistic images to crafting natural text, you will explore hands-on examples while tackling important ethical questions. This book gives you the skills and confidence to explore the limitless potential of generative AI. KEY FEATURES ● Learn GANs, VAEs, and Transformers with real-world applications. ● Build scalable generative AI models using AWS, Azure, and GCP. ● Explore ethical AI, creative projects, and future trends in technology. WHAT YOU WILL LEARN ● Build foundational knowledge of generative AI principles and models. ● Apply machine learning and deep learning for creative content generation. ● Leverage GANs, VAEs, and Transformer models in real-world scenarios. ● Master cloud tools for scalable generative AI development. ● Address ethical challenges and implement responsible AI practices. ● Explore advanced applications and future directions of generative AI WHO THIS BOOK IS FOR This book is designed for data scientists, machine learning engineers, software developers, cloud professionals, educators, students, and creative professionals. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative Adversarial Networks 3. Variational Autoencoders 4. Transformer Models and Language Generation 5. Image Generation and Style Transfer 6. Text Generation and Language Models with Real-time Examples 7. Generative AI in Art and Creativity 8. Exploring Advanced Concepts 9. Future Direction and Challenges 10. Building Your Own-Generative AI Models 11. Conclusion and Outlook Appendices

DESCRIPTION Generative AI is revolutionizing the way we interact with technology. Imagine creating hyper-realistic images, composing original music pieces, or generating creative text formats, all with the help of AI. This book provides a comprehensive exploration of generative AI and its transformative impact across various industries. This book begins with the basics of AI, explaining ML and design patterns to build a solid foundation. It delves deeply into generative AI and then progresses through machine learning, deep learning, and essential architectures such as CNNs, GANs, Diffusion, RNNs, LSTMs, and Transformers. It covers practical applications, from regression and classification to advanced use cases such as image generation, editing, document search, content summarization, and question answering. Readers will also learn to build prototypes like a Document Q&A bot, research assistant, and prompt playground, while mastering techniques such as continued pre-training, fine-tuning, model merging, retrieval-augmented generation, and agentic AI. By the end of this book, you will transform from a curious beginner to a confident, generative AI user. You will possess the knowledge and skills to explore its capabilities for creative expression, problem-solving, and even business innovation. You will be able to confidently navigate the world of generative AI, turning your ideas into reality. KEY FEATURES ● Explore the entire spectrum of generative AI, from fundamental AI concepts to advanced LLM applications. ● Includes practical examples, code snippets, and real-world case studies to enhance learning and understanding. ● Learn how to use generative AI for business applications, including ethical considerations. WHAT YOU WILL LEARN ● Explore concepts of AI, ML, deep learning, and generative AI. ● Learn about computer vision and generative image AI supported by coding examples. ● Discover NLP Techniques, Transformer architecture components and generative text AI supported by coding examples. ● Understand prompt engineering and LLM frameworks while building prototypes. ● Examine the role of LLM operations throughout the entire LLM lifecycle. ● Investigate the potential impact of generative AI on enterprises and develop business strategies. WHO THIS BOOK IS FOR This book is ideal for anyone curious about generative AI, regardless of their prior technical expertise. Whether you are a business professional, a student, an artist, or simply someone fascinated by the future of technology, this book will provide you with a clear and accessible understanding of this groundbreaking field. TABLE OF CONTENTS 1. AI Fundamentals 2. GenAI Foundation 3. GenAI for Images 4. Transforming Images with GenAI 5. GenAI for Text 6. ChatGPT 7. Large Language Model Frameworks 8. Large Language Model Operations 9. Generative AI for the Enterprise 10. Advances and Sustainability in Generative AI

Generative AI and ChatGPT have the potential to transform industries and society by improving efficiency, enhancing creativity, and enabling more personalized experiences. If you are someone who is looking to stay ahead of the curve in this rapidly evolving digital age and utilize its potential, this book is for you. This book gives a comprehensive overview of Generative AI and ChatGPT, the cutting-edge technologies that have gained significant attention in recent times. The book aims to provide a thorough understanding of these technologies, architectures, and training methods, including their background, development process, and current state. The book helps discover innovative ways in which these technologies have been implemented to achieve measurable benefits, including improved efficiency, customer satisfaction, security, and revenue growth and its potential application across different industries and use cases.

DESCRIPTION Agentic AI represents a paradigm shift in artificial intelligence, moving beyond simple chatbots to autonomous systems that can reason, plan, and execute complex tasks with minimal human intervention. As organizations race to harness this transformative technology, the demand for practical guidance on building production-ready AI agents has never been greater. This comprehensive guide takes you through the complete journey of agentic AI development. Beginning with foundational concepts and theoretical frameworks, you will explore how large language models serve as cognitive cores for intelligent agents. The book covers essential design patterns, tool integration, and memory systems before diving into engineering practices, including development lifecycle, performance optimization, testing, and observability. You will learn deployment strategies, security considerations, and ethical frameworks essential for responsible AI development. The final chapters showcase real-world enterprise and consumer applications and predict the future evolution of the technology and its use cases. By the end of this book, you will possess the skills to architect, build, deploy, and maintain sophisticated agentic AI systems. Whether you are developing internal enterprise tools or customer-facing AI products, this guide provides the practical knowledge needed to bring autonomous AI agents from concept to production. WHAT YOU WILL LEARN ● Architect multi-agent systems using proven design patterns. ● Integrate LLMs as cognitive cores for autonomous agents. ● Build secure tool interfaces and external integrations. ● Deploy and observe agents in production environments. ● Apply ethical frameworks for responsible AI development. ● Optimize agent performance through testing and observability. WHO THIS BOOK IS FOR This book targets software developers, students, and engineers building agentic AI products. It serves as a strategic reference for business leaders and executives making investment decisions. TABLE OF CONTENTS 1. Introduction to Agentic Systems 2. Theoretical Frameworks 3. The Agentic Ecosystem 4. Large Language Models as Cognitive Cores 5. Agent Patterns and Design Paradigms 6. Tools and Interfaces 7. Memory Systems 8. Development Lifecycle 9. Performance Optimization 10. Testing and Quality Assurance 11. Monitoring and Observability 12. Deployment Strategies 13. Security and Safety 14. Ethical and Responsible Development 15. Enterprise Applications 16. Consumer Applications 17. Future Directions 18. Example of Conversational Agent

Unveiling the power and potential of Generative AI for a limitless future KEY FEATURES ● Holistic and accessible journey into Generative AI. ● Indispensable guide for unleashing Generative AI potential. ● Transforming technology, business, art, and innovation, covering technological advancements and business optimization. DESCRIPTION The Potential of Generative AI invites you for a captivating journey into the revolutionary technology, where machines become co-creators and the line between imagination and reality blurs. You will learn how AI helps doctors, engineers, and scientists solve real-world problems. Next, you will explore use cases where ChatGPT can boost productivity and enhance creativity. The book explores the journey from the origins of this revolutionary technology to its cutting-edge applications. Discover how generative models like GANs and VAEs work, and familiarize yourself with the impact they are making in fields like healthcare, finance, and art. Through real-world case studies and engaging examples, you will witness AI generating life-saving drugs, composing music, and even designing innovative products. This book explores the cutting-edge capabilities and potential of generative AI in the tech landscape. It will help you discover how generative AI can unlock new opportunities and enhance business operations. WHAT YOU WILL LEARN ● Learn about the different types of generative models, how they work, and their impact across various industries including healthcare, finance, and entertainment. ● Explore the creative potential of generative AI in art, music, and design. ● Develop Generative Adversarial Networks (GANs), with a focus on their architecture, training process, and real-world applications. ● Build and deploy generative models, ensuring readers to leverage this powerful technology. ● Perfect the art of generating text, images, music, and even code with AI, utilize your creative potential. WHO THIS BOOK IS FOR This book is for artists, programmers, musicians, designers, writers, researchers, entrepreneurs, scientists, Machine Learning practitioners and dreamers of all sorts. Generative AI awaits and is ready to transform your craft and empower your vision. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative AI in Industries 3. Fundamentals of Generative Models 4. Applications Across Industries 5. Creative Expression with Generative AI 6. Generative AI in Business and Innovation 7. Deep Dive into GANs 8. Building and Deploying Generative Models

DESCRIPTION Building Intelligent Applications with Generative AI is a comprehensive guide that unlocks the power of generative AI for building cutting-edge applications. This book covers a wide range of use cases and practical examples, from text generation and conversational agents to creative media generation and code completion. These examples are designed to help you capitalize on the potential of generative AI in your applications. Through clear explanations, step-by-step tutorials, and real-world case studies, you will learn how to prepare data and train generative AI models. You will also explore different generative AI techniques, including large language models like GPT-4, ChatGPT, Llama 2, and Google’s Gemini, to understand how they can be applied in various domains, such as content generation, virtual assistants, and code generation. With a focus on practical implementation, this book also examines ethical considerations, best practices, and future trends in generative AI. Further, this book concludes by exploring ethical considerations and best practices for building responsible GAI applications, ensuring you are harnessing this technology for good. By the end of this book, you will be well-equipped to leverage the power of GAI to build intelligent applications and unleash your creativity in innovative ways. KEY FEATURES ● Learn the fundamentals of generative AI and the practical usage of prompt engineering. ● Gain hands-on experience in building generative AI applications. ● Learn to use tools like LangChain, LangSmith, and FlowiseAI to create intelligent applications and AI chatbots. WHAT YOU WILL LEARN ● Understand generative AI (GAI) and large language models (LLMs). ● Explore real-world GAI applications across industries. ● Build intelligent applications with the ChatGPT API. ● Explore retrieval augmented generation with LangChain and Gemini Pro. ● Create chatbots with LangChain and Streamlit for data retrieval. WHO THIS BOOK IS FOR This book is for developers, data scientists, AI practitioners, and tech enthusiasts who are interested in leveraging generative AI techniques to build intelligent applications across various domains. TABLE OF CONTENTS 1. Exploring the World of Generative AI 2. Use Cases for Generative AI Applications 3. Mastering the Art of Prompt Engineering 4. Integrating Generative AI Models into Applications 5. Emerging Trends and the Future of Generative AI 6. Building Intelligent Applications with the ChatGPT API 7. Retrieval Augmented Generation with Gemini Pro 8. Generative AI Applications with Gradio 9. Visualize your Data with LangChain and Streamlit 10. Building LLM Applications with Llama 2 11. Building an AI Document Chatbot with Flowise AI 12. Best Practices for Building Applications with Generative AI 13. Ethical Considerations of Generative AI

DESCRIPTION GenAI and Python are changing how we use technology, making it essential to understand both to stay innovative and work efficiently. GenAI significantly impacts learning Python by generating personalized code snippets, accelerating the learning process. This book bridges the gap between traditional education and the practical challenges students encounter today. It combines hands-on learning with modern GenAI tools like GPT-4 and Copilot. The book begins with fundamental GenAI concepts, including GPT-4 and Gemini, and mastering prompt engineering for optimal GenAI interaction. Instead of starting with technical details like algorithms and syntax, it introduces coding through interactive, practical Python Jupyter Notebooks and Google Colab projects. Readers will learn Python code with a calculator application, explore fundamental sorting algorithms, and manipulate data using Pandas. The book then explores advanced ML through CNN image classification with Fast.ai, and deploying AI models as web applications using Hugging Face and Gradio. It also addresses critical ethical considerations in AI, focusing on fairness and bias, and provides career guidance for modern programmers. Moreover, this book takes a fresh approach to learning by prioritizing exploration and creativity, much like the way Gen Z engage with games, apps, and hands-on activities. By the end of this book, you will be equipped with the practical skills and ethical understanding to confidently apply Python and GenAI in diverse projects, helping you navigate the evolving landscape of AI-driven development. WHAT YOU WILL LEARN ● Write and debug Python code through hands-on projects. ● Learn GenAI setup, and effective prompt engineering. ● Step-by-step Python projects using Jupyter Notebooks and GenAI. ● Deploy AI models as interactive web applications using Hugging Face and Gradio frameworks. ● Leverage GenAI tools like GPT-4 and Copilot. ● Understand AI bias and use it responsibly for positive impact. WHO THIS BOOK IS FOR This book is for professionals interested in learning Python and using GenAI tools like GPT-4 in practical applications. It is for aspiring programmers, students, and data analysts seeking practical Python and GenAI skills. TABLE OF CONTENTS 1. Introduction to GenAI 2. Jupyter Notebook 3. Dissect The Calculator App 4. Sorting on My Mind 5. Pandas, the Data Tamer 6. Decipher CNN App 7. Gradio and Hugging Face Deployment 8. Fairness and Bias 9. Your Turn to Be a Code Walker

DESCRIPTION Generative AI and agentic AI are reshaping how we interact with data, enabling intelligent systems that can reason, generate, and autonomously act across multiple modalities. From text and images to voice and structured data, these technologies are increasingly essential in enterprise and research applications today. This book offers a complete roadmap to mastering multimodal generative AI and agentic AI systems. It covers foundational concepts, vision-language models, retrieval-augmented generation, human-in-the-loop and multi-agent workflows, text-to-SQL, OCR, and hybrid AI integrations. Each chapter combines theory, practical guidance, code implementations, and real-world case studies, helping readers understand architectures, pipelines, and production-grade deployments. By the end of this book, readers will be capable of designing, implementing, and scaling robust multimodal and agentic AI systems. They will gain hands-on expertise in reasoning, generation, retrieval, agent orchestration, and Ops, equipping them to build production-ready AI applications and excel in their roles. WHAT YOU WILL LEARN ● Understand multimodal generative AI and agentic AI systems. ● Architecting RAG, vector DBs, embeddings, cross-encoders, and core agentic planning. ● Build retrieval-augmented generation workflows efficiently. ● Implement human-in-the-loop and multi-agent pipelines. ● Apply text-to-SQL for real-time data queries. ● Develop OCR solutions for images and documents. ● Integrate traditional ML models with GenAI workflows. ● Deploy production-grade AI with monitoring and observability. WHO THIS BOOK IS FOR This book is for AI enthusiasts, data scientists, ML engineers, and software developers with foundational Python and ML knowledge. It is ideal for tech leads, researchers, and enterprise architects aiming to build and scale multimodal, production-grade agentic AI systems. TABLE OF CONTENTS 1. Introducing New Age Generative AI 2. Deep Dive into Multimodal Systems 3. Implementing Unimodal Local GenAI System 4. Implementing Unimodal API-based GenAI Systems 5. Implementing Agentic GenAI Systems with Human-in-the-loop 6. Two and Multi-stage GenAI Systems 7. Building a Bidirectional Multimodal Retrieval System 8. Building a Multimodal RAG System 9. Building GenAI Systems with Reranking 10. Retrieval Optimization for Multimodal GenAI 11. Building Multimodal GenAI Systems with Voice as Input 12. Advanced Multimodal GenAI Systems 13. Advanced Multimodal GenAI Systems Implementation 14. Building Text-to-SQL Systems 15. Agentic Text-to-SQL Systems and Architecture Decision-Making 16. GenAI for Extracting Text from Images 17. Integrating Traditional AI/ML into GenAI Workflow 18. LLM Operations and GenAI Evaluation Techniques

DESCRIPTION Over the past few years, we have seen leaps and strides in ML and most recently generative AI. Companies and software teams are rushing to enhance, rebuild, and create new software offerings with this new intelligence. As they innovate and create delightful new experiences for their customers new challenges arise. Understanding how these applications work and how to use state-of-the-art infrastructure tools like Kubernetes will help organizations and professionals succeed with this new technology. The book covers essential technical implementations from ML fundamentals through advanced deployment strategies, focusing on practical patterns. Core topics include Kubernetes-native GPU scheduling and resource management, MLOps pipeline architectures using Kubeflow/MLflow, and advanced model serving patterns. It details data management architectures, vector databases, and RAG systems, alongside monitoring solutions with Prometheus/Grafana. Finally, we will look at some advanced concerns for production in the realm of security and data reliability. After reading this book, you will be equipped with a broad knowledge of the end-to-end generative AI pipeline and how Kubernetes can be leveraged to run your generative AI workloads at scale in the real-world. KEY FEATURES ● Learn how Kubernetes can help you run your generative AI workloads. ● Using hands-on examples, you will work with real-world foundational models and a variety of tools and capabilities in the K8s ecosystem. ● A broad survey of both generative AI and Kubernetes in one book. WHAT YOU WILL LEARN ● How to evaluate and compare models for new applications and use cases. ● How Kubernetes can add reliability and scale to your AI applications. ● What does an AI delivery pipeline contain and how to start one. ● How AI models encode words and work with natural language. ● How prompting and refinement techniques can improve results. ● How to use your own data to augment AI responses. WHO THIS BOOK IS FOR This book is for teams building new applications or new functionality with generative AI, but want to better understand the infrastructure needed to bring their AI applications to production. This book is also for shared services, infrastructure, or cybersecurity teams who provide platforms and infrastructure for application, or product development. TABLE OF CONTENTS 1. Introduction to Generative Artificial Intelligence 2. Kubernetes for Generative AI 3. Introduction to Foundational Models on Kubernetes 4. Working with Foundational Models 5. Process and Pipelines 6. Process and Pipelines on Kubernetes 7. Managing Data for Generative AI 8. Refining and Improving Results 9. Observability and Monitoring 10. Securing ML/GenAI Pipelines on K8s

DESCRIPTION GenAI is changing how we build software, and learning to master these models with Python is essential for any modern developer. This book is a comprehensive guide to the intricate world of GenAI. This book serves as a complete guide to GenAI, covering foundational concepts and advanced system design. It begins with core principles of generative modeling, explaining probabilistic approaches and how they differ from traditional machine learning methods. The journey then moves into GANs, including their architecture, variants, training challenges, and evaluation techniques. Readers are introduced to VAEs, focusing on latent space representation, probabilistic learning, and practical design strategies. It then explores transformer architectures and Vision Transformers, explaining how attention mechanisms enable modern generative and multimodal systems. Hybrid approaches combining VAEs and transformers are also covered to demonstrate real-world model design. By the end of this book, you will master the math and engineering behind GANs and VAEs to build high-quality visual synthesis networks. You will gain hands-on expertise in Vision Transformers, diffusion models, and scalable MLOps infrastructure using vector and graph databases. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting-edge techniques in generative AI. IN THIS EDITION, YOU WILL LEARN HOW TO: ● Master generative math to stabilize adversarial and stochastic vision networks. ● Build hybrid attention systems using transformers and diffusion architectures. WHO THIS BOOK IS FOR This book is designed for current and aspiring AI and deep learning professionals, architects, students, and anyone looking to begin a rewarding career in GenAI. It is ideal for those who want to build a strong foundation and progress from traditional generative models to modern, industry-grade GenAI systems and applications. TABLE OF CONTENTS 1. Introducing Generative AI 2. Designing Generative Adversarial Networks 3. Training and Developing Generative Adversarial Networks 4. Architecting Autoencoder for Generative Artificial Intelligence 5. Building and Training Generative Autoencoders 6. Designing Generative Variational Autoencoder 7. Building Variational Autoencoders for Generative AI 8. Fundamentals of Designing New Age Generative Vision Transformer 9. Implementing Generative Vision VAE Transformer 10. Architectural Refactoring for Generative Modeling 11. Major Technical Roadblocks in Generative AI and Way Forward 12. Overview and Application of Generative AI Models 13. Generative AI and LLM Extended 14. Generative AI and LLM Advanced 15. Finetuning LLMs 16. Mixture-of-Experts 17. Diffusion Models 18. Practical Finetuning of LLM

Generative AI is revolutionizing how we interact with technology, empowering us to create everything from compelling text to intricate code. This book is your practical guide to harnessing the power of open-source libraries, enabling you to build cutting-edge generative AI applications without needing extensive prior experience. In this book, you will journey from foundational concepts like natural language processing and transformers to the practical implementation of large language models. Learn to customize foundational models for specific industries, master text embeddings, and vector databases for efficient information retrieval, and build robust applications using LangChain. Explore open-source models like Llama and Falcon and leverage Hugging Face for seamless implementation. Discover how to deploy scalable AI solutions in the cloud while also understanding crucial aspects of data privacy and ethical AI usage. By the end of this book, you will be equipped with technical skills and practical knowledge, enabling you to confidently develop and deploy your own generative AI applications, leveraging the power of open-source tools to innovate and create. WHAT YOU WILL LEARN ● Building AI applications using LangChain and integrating RAG. ● Implementing large language models like Llama and Falcon. ● Utilizing Hugging Face for efficient model deployment. ● Developing scalable AI applications in cloud environments. ● Addressing ethical considerations and data privacy in AI. ● Practical application of vector databases for information retrieval. WHO THIS BOOK IS FOR This book is for aspiring tech professionals, students, and creative minds seeking to build generative AI applications. While a basic understanding of programming and an interest in AI are beneficial, no prior generative AI expertise is required. TABLE OF CONTENTS 1. Getting Started with Generative AI 2. Overview of Foundational Models 3. Text Processing and Embeddings Fundamentals 4. Understanding Vector Databases 5. Exploring LangChain for Generative AI 6. Implementation of LLMs 7. Implementation Using Hugging Face 8. Developments in Generative AI 9. Deployment of Applications 10. Generative AI for Good

DESCRIPTION Agentic AI is rapidly emerging as the next frontier in artificial intelligence, enabling systems to act autonomously, adapt in real time, and make goal-driven decisions. In today’s fast-evolving digital landscape, this technology is reshaping industries by powering intelligent workflows, enhancing efficiency, and unlocking new opportunities for innovation. This book provides a structured journey from foundational concepts to advanced implementation. It begins with the evolution of AI and the defining principles of agentic systems, then explores agent properties such as memory, personas, and lifecycle management. Readers will gain insights into enabling technologies like LLMs and reinforcement learning, followed by architectural patterns for building scalable agents. The chapters provide hands-on evaluations of frameworks like AutoGen, CrewAI, and LangGraph, alongside strategies for data governance and industry-specific use cases in finance and healthcare, such as fraud detection, healthcare personalization, and supply chain optimization. Through a delivery bot case study, you will master implementation, scaling, and AgentOps. Finally, we cover production readiness using containerization and LLM-as-a-judge evaluation to prepare you for the path to AGI. By the end of this book, you will be competent in designing, securing, and deploying professional AI agents. You will possess the practical skills to transition from concept to production, ready to lead innovation in the agentic age. WHAT YOU WILL LEARN ● Understand core principles that define modern agentic AI systems. ● Learn step-by-step methods for designing enterprise agent workflows. ● Discover data foundations needed to enable reliable autonomous agents. ● Explore architecture patterns for scalable multi-agent applications. ● Build practical skills to prototype, test, and deploy AI agents. ● Apply observability and monitoring principles for auditing agent behavior. ● Identify real industry use cases and implementation best practices. WHO THIS BOOK IS FOR This book is for technology leaders, architects, and developers who possess a solid foundation in Python and basic AI concepts. It serves product teams and practitioners needing professional guidance to design, scale, and responsibly deploy autonomous multi-agent systems. TABLE OF CONTENTS 1. Introduction to Agentic AI 2. Essential Concepts of Agentic AI 3. Core Technologies Enabling Agentic AI 4. Agentic AI Architecture 5. Data Management for Agentic AI 6. Frameworks for Agentic AI-Part 1 7. Frameworks for Agentic AI-Part 2 8. Value Add with Agentic AI 9. Practical Example: Case Study 10. Implementation Approach to Agentic AI 11. Productionizing Agentic AI 12. Future of Agentic AI Glossary Bibliography Further Readings