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The comprehensive content of the book, “Transforming Business with Generative AI”, unfolds with an in-depth exploration of Generative AI, establishing its transformative potential in the business landscape. Covering key concepts and historical evolution, the book provides a foundation in machine learning, neural networks, and deep learning, tracing the journey from rule-based systems to the contemporary neural network revolution. It highlights the pivotal role of hardware advancements. The author, Rajiv Tulsyan, then delves into the profound impact of Generative AI on businesses, showcasing its applications in automating tasks, content generation, personalization, data analysis, and creative innovation. Real-world case studies, including Netflix's recommendation system and e-commerce product descriptions, illustrate its practical implications across diverse industries. Each chapter incorporates case studies and real-world examples to reinforce theoretical concepts, making the book accessible to both beginners and professionals. The inclusion of future trends and challenges anticipates the evolving landscape of Generative AI. The book, “Transforming Business with Generative AI”, concludes by emphasizing the importance of addressing ethical concerns and staying informed about emerging trends for businesses seeking to fully leverage Generative AI's potential.

Today, the convergence of Artificial Intelligence (AI) and Big Data has revolutionized industries worldwide, driving business growth and reshaping societies. While these technologies have yielded remarkable benefits, many developing countries face challenges in harnessing their potential due to inadequate data collection and availability. Emerging Advancements in AI and Big Data Technologies in Business and Society delves into the profound impact of AI and Big Data on the digital landscape and their transformative influence on social, economic, and political spheres. With a historical overview of AI's evolution and its operational definition, this book explores interconnected subfields such as problem-solving, intelligent agents, natural language processing, computer vision, and machine learning. AI is hailed as the fourth industrial revolution and the widespread use of AI technologies prompts discussions about their applications, performances, and societal impact. This book serves as a comprehensive guide for academics, researchers, and students in universities and engineering schools. It also caters to policymakers, government officers, corporate leaders, technology directors, and managers seeking to understand the potential of AI and Big Data. Additionally, libraries and information centers catering to the needs of these professionals will find this book an essential resource.

​ This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems.

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part "Technologies and Methods" contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part "Processes and Applications" details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.

In the rapidly evolving landscape of artificial intelligence and data management, "Harnessing Snowflake with Generative AI and LLMs" serves as a comprehensive guide for leveraging the powerful combination of Snowflake's Data Cloud and advanced generative AI technologies, particularly Large Language Models (LLMs). This book is designed for data scientists, AI practitioners, and business leaders eager to unlock the full potential of their data and AI initiatives. The book begins with an introduction to the foundational concepts of generative AI and LLMs, explaining their significance and impact on various industries. It provides a clear overview of Snowflake's unique architecture and capabilities, highlighting its ability to efficiently store, manage, and analyze large volumes of data. By integrating Snowflake with generative AI, readers will learn how to build scalable and efficient AI-driven solutions that can handle complex data processing tasks in real-time. A detailed exploration of integrating generative AI with Snowflake follows, showcasing practical applications and real-world use cases. These include enhancing customer support through advanced chatbots, automating content creation for marketing, and implementing predictive analytics for more informed business decisions. The book emphasizes best practices for using Snowflake with LLMs, covering data quality, scalability, and security considerations. Further chapters delve into the technical aspects of Snowflake, such as data warehousing, real-time data processing, and performance optimization for AI workloads. The book also addresses crucial ethical considerations, including bias, fairness, and data privacy, ensuring that readers are equipped to develop responsible and compliant AI solutions. Looking ahead, the book explores emerging technologies and future trends in generative AI and Snowflake, providing insights into the next wave of innovations. Readers will gain a thorough understanding of how to stay ahead in the AI landscape by continuously adapting to new advancements. "Harnessing Snowflake with Generative AI and LLMs" is an essential resource for anyone looking to effectively combine cutting-edge AI techniques with robust data management practices, driving transformative results in their organization.