Transforming Data Pipelines With Generative Ai And Deep Learning
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TRANSFORMING DATA PIPELINES WITH GENERATIVE AI AND DEEP LEARNING
Author: Arun Kumar Ramachandran Sumangala Devi
language: en
Publisher: BUDHA PUBLISHER
Release Date:
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DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED
.The advances in data engineering technologies, including big data infrastructure, knowledge graphs, and mechanism design, will have a long-lasting impact on artificial intelligence (AI) research and development. This paper introduces data engineering in AI with a focus on the basic concepts, applications, and emerging frontiers. As a new research field, most data engineering in AI is yet to be properly defined, and there are abundant problems and applications to be explored. The primary purpose of this paper is to expose the AI community to this shining star of data science, stimulate AI researchers to think differently and form a roadmap of data engineering for AI. Since this is primarily an informal essay rather than an academic paper, its coverage is limited. The vast majority of the stimulating studies and ongoing projects are not mentioned in the paper.
AI-Driven Enterprise Architecture: From Data Engineering to Generative AI 2025
Author: Author:1- Bhanuvardhan Nune, Author:2-Dr. Gaurav Kumar
language: en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date:
PREFACE In the rapidly evolving landscape of technology, enterprises are increasingly turning to artificial intelligence (AI) to drive innovation, efficiency, and growth. The integration of AI into enterprise architecture has shifted from a trend to an essential strategy for businesses looking to maintain a competitive edge. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is written to explore the transformative impact of AI across all layers of enterprise systems, from data engineering and analytics to innovative generative AI technologies that are reshaping industries. In today’s digital age, businesses face an explosion of data that is often unstructured, decentralized, and sold. For AI to truly revolutionize enterprise systems, there must be a solid architecture that not only supports large-scale data processing but also enables the seamless integration of AI technologies into every corner of the organization. This book takes a comprehensive approach to AI-driven enterprise architecture, focusing on the technical, strategic, and operational challenges and opportunities associated with AI adoption. The journey from data engineering to generative AI requires a solid foundation of data management and processing capabilities. The book begins by discussing the critical importance of data engineering, the practice of building robust systems for collecting, storing, and transforming data into actionable insights. Understanding how to build and maintain efficient data pipelines, databases, and data lakes forms the backbone of AI integration in an enterprise. This foundational understanding sets the stage for deploying machine learning (ML) models and AI-driven tools, which require sophisticated infrastructure to function on a scale. The integration of machine learning and AI models into enterprise architecture is the central focus of this book. As businesses recognize the value of AI in improving decision-making, automation, and customer experiences, this book guides readers through how to implement AI across multiple enterprise functions. From predictive analytics and automation to natural language processing (NLP) and computer vision, we will examine how these AI technologies interact with existing enterprise systems to create smarter, more efficient business operations. One of the most exciting and rapidly advancing fields in AI is generative AI—a technology that can create new data, designs, or content based on learned patterns. Generative AI tools like GPT-3, DALL-E, and stable diffusion models are now being used to generate text, images, code, and even video. The power of these models lies in their ability to produce new, high-quality content that can be harnessed for marketing, customer engagement, product development, and innovation. This book explores how generative AI fits within the broader enterprise architecture and how businesses can leverage these capabilities to unlock new value streams, foster creativity, and enhance productivity. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is designed for business leaders, data engineers, architects, and AI practitioners who are looking to understand the potential of AI in their organizations. Through real-world case studies, best practices, and technical insights, this book aims to provide a holistic view of how AI-driven enterprise architecture can deliver long-term strategic value. The book also delves into the challenges and ethical considerations of AI implementation, particularly with regard to data privacy, algorithmic bias, and governance, ensuring that AI is deployed responsibly and sustainably. As businesses embrace AI technologies, it is clear that the future of enterprise architecture will be driven by data-centric, AI-powered models that allow organizations to be more adaptive, responsive, and innovative. This book offers a roadmap for navigating that future, helping organizations transform their architecture to support the AI-driven, intelligent enterprise of tomorrow. We invite you to embark on this journey through the evolving world of AI-driven enterprise architecture, where the combination of data engineering, machine learning, and generative AI is shaping the future of businesses across the globe. Authors