The Art Of Data Engineering Building Ai Driven Pipelines And Intelligent Systems
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The Art of Data Engineering: Building AI-Driven Pipelines and Intelligent Systems
Author: Muneer Ahmed Salamkar
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-02-28
In the age of AI, the backbone of intelligent systems lies in the seamless flow of high-quality data. The Art of Data Engineering: Building AI-Driven Pipelines and Intelligent Systems is an essential guide for data engineers, AI practitioners, and technology leaders seeking to design scalable, efficient, and intelligent data ecosystems. This book explores the critical role of data engineering in AI success, offering a comprehensive framework for building robust data pipelines that power machine learning models and real-time decision-making systems. From foundational concepts to advanced techniques, readers will learn how to design modular pipelines, leverage real-time analytics, and optimize data storage solutions using cutting-edge tools like Apache Kafka, Spark, and Databricks. With practical case studies across industries such as finance, healthcare, and e-commerce, the book demonstrates how intelligent data systems transform raw data into actionable insights. Key topics include data transformation, feature engineering, cloud-based architectures, and ethical considerations in AI. Whether you're architecting real-time fraud detection systems or developing recommendation engines, The Art of Data Engineering equips professionals with the skills to design resilient pipelines that drive innovation. This book is your definitive roadmap to mastering the intersection of data engineering and AI, empowering you to build the next generation of intelligent systems.
Data Engineering for AI/ML Pipelines
Author: Venkata Karthik Penikalapati
language: en
Publisher: BPB Publications
Release Date: 2024-10-18
DESCRIPTION Data engineering is the art of building and managing data pipelines that enable efficient data flow for AI/ML projects. This book serves as a comprehensive guide to data engineering for AI/ML systems, equipping you with the knowledge and skills to create robust and scalable data infrastructure. This book covers everything from foundational concepts to advanced techniques. It begins by introducing the role of data engineering in AI/ML, followed by exploring the lifecycle of data, from data generation and collection to storage and management. Readers will learn how to design robust data pipelines, transform data, and deploy AI/ML models effectively for real-world applications. The book also explains security, privacy, and compliance, ensuring responsible data management. Finally, it explores future trends, including automation, real-time data processing, and advanced architectures, providing a forward-looking perspective on the evolution of data engineering. By the end of this book, you will have a deep understanding of the principles and practices of data engineering for AI/ML. You will be able to design and implement efficient data pipelines, select appropriate technologies, ensure data quality and security, and leverage data for building successful AI/ML models. KEY FEATURES ● Comprehensive guide to building scalable AI/ML data engineering pipelines. ● Practical insights into data collection, storage, processing, and analysis. ● Emphasis on data security, privacy, and emerging trends in AI/ML. WHAT YOU WILL LEARN ● Architect scalable data solutions for AI/ML-driven applications. ● Design and implement efficient data pipelines for machine learning. ● Ensure data security and privacy in AI/ML systems. ● Leverage emerging technologies in data engineering for AI/ML. ● Optimize data transformation processes for enhanced model performance. WHO THIS BOOK IS FOR This book is ideal for software engineers, ML practitioners, IT professionals, and students wanting to master data pipelines for AI/ML. It is also valuable for developers and system architects aiming to expand their knowledge of data-driven technologies. TABLE OF CONTENTS 1. Introduction to Data Engineering for AI/ML 2. Lifecycle of AI/ML Data Engineering 3. Architecting Data Solutions for AI/ML 4. Technology Selection in AI/ML Data Engineering 5. Data Generation and Collection for AI/ML 6. Data Storage and Management in AI/ML 7. Data Ingestion and Preparation for ML 8. Transforming and Processing Data for AI/ML 9. Model Deployment and Data Serving 10. Security and Privacy in AI/ML Data Engineering 11. Emerging Trends and Future Direction
Reimagining Tax and Advisory Services: Intelligent Systems, Security, and Data- Driven Decision Making
Author: Pallav Kumar Kaulwar
language: en
Publisher: Deep Science Publishing
Release Date: 2025-05-07
The tax and advisory landscape is undergoing a profound transformation. Rapid advancements in artificial intelligence (AI), data analytics, and cybersecurity are redefining how professionals deliver value in an increasingly complex regulatory and financial environment. This book, Reimagining Tax and Advisory Services: Intelligent Systems, Security, and Data-Driven Decision Making, explores how digital intelligence is reshaping the traditional roles of tax advisors, auditors, and financial consultants. As regulatory frameworks evolve and businesses demand faster, more accurate insights, the need for real-time, data-driven decision making has never been greater. Intelligent systems—powered by AI, machine learning, and robotic process automation—are now capable of analyzing vast datasets, interpreting tax laws, and offering predictive insights with a speed and precision that far surpass human capabilities. These technologies are not just enhancing productivity; they are reimagining the core functions of tax and advisory services. This book takes a multidimensional approach to understanding this shift. It explores how secure, intelligent platforms are enabling seamless compliance, fraud detection, and strategic financial planning. It also examines how cybersecurity, data governance, and ethical AI are essential pillars in building client trust and maintaining the integrity of advisory services in a digital-first world. From intelligent tax engines to automated audit trails, and from AI-powered client advisory portals to integrated DevSecOps practices, we present a future-ready blueprint for firms looking to thrive in the age of digital finance. Real-world use cases, emerging trends, and actionable frameworks offer both strategic guidance and practical tools for professionals navigating this complex transition. Whether you are a tax consultant, financial advisor, IT architect, or decision-maker in a professional services firm, this book offers a timely lens into the technologies and principles driving innovation in the sector. Our aim is not just to inform—but to inspire a reinvention of tax and advisory services for the intelligent, secure, and data-driven era ahead.