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

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Author : Muneer Ahmed Salamkar
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-02-28

The Art Of Data Engineering Building Ai Driven Pipelines And Intelligent Systems written by Muneer Ahmed Salamkar and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-28 with Computers categories.

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.

Reimagining Tax And Advisory Services Intelligent Systems Security And Data Driven Decision Making

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Author : Pallav Kumar Kaulwar
language : en
Publisher: Deep Science Publishing
Release Date : 2025-05-07

Reimagining Tax And Advisory Services Intelligent Systems Security And Data Driven Decision Making written by Pallav Kumar Kaulwar and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Business & Economics categories.

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.

Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation

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Author : Phanish Lakkarasu
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-06

Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation written by Phanish Lakkarasu and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.

In today’s fast-paced digital era, organizations are under constant pressure to innovate, scale, and deliver intelligent services with speed and reliability. Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI-Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation is a comprehensive exploration into the foundational and advanced components required to build robust, future-ready cloud ecosystems. This book is the product of years of observing the shifting paradigms in enterprise IT—from legacy systems and monolithic architectures to microservices, serverless computing, and AI-powered infrastructure. At the heart of this evolution lies the need for cloud-native platforms that are not only scalable and resilient but also intelligent and automation-ready. The content in these pages is aimed at architects, engineers, data scientists, DevOps professionals, and digital transformation leaders who seek to understand and implement the key building blocks of modern cloud systems. It delves into the design principles behind scalable infrastructure, best practices for integrating AI and Machine Learning, and the implementation of MLOps pipelines to streamline deployment, monitoring, and continuous improvement of ML models. Furthermore, it provides practical insights into data engineering strategies that ensure secure, efficient, and real-time data flow across distributed environments. We also explore critical topics such as multi-cloud and hybrid cloud strategies, edge computing, observability, cost optimization, and governance—ensuring that readers are equipped to tackle both the technical and operational challenges of building next-generation platforms. What sets this book apart is its unified approach to cloud, AI, and data engineering—treating them not as isolated silos but as interconnected pillars of intelligent digital transformation. Whether you are designing enterprise-grade solutions or modernizing existing infrastructures, this guide will serve as your companion in navigating complexity with clarity and confidence.

Data Engineering For Ai Ml Pipelines

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Author : Venkata Karthik Penikalapati
language : en
Publisher: BPB Publications
Release Date : 2024-10-18

Data Engineering For Ai Ml Pipelines written by Venkata Karthik Penikalapati and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Computers categories.

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

Complete Data Engineering In 8 Hours

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Author : QuickTechie | A career growth machine
language : en
Publisher: PappuPass Learning Resources
Release Date : 2025-02-02

Complete Data Engineering In 8 Hours written by QuickTechie | A career growth machine and has been published by PappuPass Learning Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.

"Complete Data Engineering in 8 Hours" is a fast-paced learning guide designed to equip both beginners and experienced professionals with the essential skills required to excel in the field of data engineering. In today's digital age, data is paramount, driving decision-making, automation, and innovation. As QuickTechie.com emphasizes, the role of a Data Engineer is increasingly vital for organizations needing to manage, process, and analyze large volumes of data effectively. This book addresses the growing need for skilled professionals who can navigate the complexities of modern data infrastructure. This book offers a structured approach, providing practical insights into core data engineering concepts. It covers essential areas such as databases, data pipelines, Extract, Transform, Load (ETL) processes, big data technologies, and cloud platforms. Unlike traditional lengthy textbooks, this guide is designed to provide a quick yet comprehensive understanding within a targeted timeframe, allowing readers to quickly grasp fundamental principles and advanced techniques. Readers can expect to follow a step-by-step learning path, mastering the art of designing, building, and scaling data systems efficiently. The book ensures readers gain practical, industry-relevant skills that can be immediately applied in a professional setting. This makes it an excellent resource for those transitioning into the field, those aiming to upskill in their current roles, or individuals preparing for data engineering job interviews. By the end of "Complete Data Engineering in 8 Hours," readers will possess the knowledge and confidence to develop, implement, and optimize data infrastructure. This will empower them to become highly valued assets in the data-driven world, capable of contributing significantly to an organization's data strategies. The book is not just a theoretical guide; it provides hands-on learning opportunities to translate theoretical knowledge into practical skills, aligning with QuickTechie.com commitment to practical, applicable technology learning.

Building Machine Learning Pipelines

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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13

Building Machine Learning Pipelines written by Hannes Hapke and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Architecting Intelligent Cloud Systems Ai Mlops And Scalable Infrastructure For The Future

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Author : PHANISH LAKKARASU
language : en
Publisher: Global Pen Press UK PUBLICATION
Release Date :

Architecting Intelligent Cloud Systems Ai Mlops And Scalable Infrastructure For The Future written by PHANISH LAKKARASU and has been published by Global Pen Press UK PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Architecture categories.

Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow

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Author : Manoj Kumar
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-30

Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow written by Manoj Kumar and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.

Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges Key Features● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow.● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action.● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines.● Offers proven strategies to optimize workflows and avoid common pitfalls. Book DescriptionIn today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. What you will learn● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.● Build and deploy real-time data processing solutions for timely and actionable insights.● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. Table of ContentsSECTION 11. Introducing Data Engineering with Databricks2. Setting Up a Databricks Environment for Data Engineering3. Working with Databricks Utilities and ClustersSECTION 24. Extracting and Loading Data Using Databricks5. Transforming Data with Databricks6. Handling Streaming Data with Databricks7. Creating Delta Live Tables8. Data Partitioning and Shuffling9. Performance Tuning and Best Practices10. Workflow Management11. Databricks SQL Warehouse12. Data Storage and Unity Catalog13. Monitoring Databricks Clusters and Jobs14. Production Deployment Strategies15. Maintaining Data Pipelines in Production16. Managing Data Security and Governance17. Real-World Data Engineering Use Cases with Databricks18. AI and ML Essentials19. Integrating Databricks with External Tools Index

I Heart Logs

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Author : Jay Kreps
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-09-23

I Heart Logs written by Jay Kreps and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-23 with Computers categories.

Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention. Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models. Go ahead and take the plunge with logs; you’re going love them. Learn how logs are used for programmatic access in databases and distributed systems Discover solutions to the huge data integration problem when more data of more varieties meet more systems Understand why logs are at the heart of real-time stream processing Learn the role of a log in the internals of online data systems Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn

Handbook Of Research On Applied Data Science And Artificial Intelligence In Business And Industry

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Author : Chkoniya, Valentina
language : en
Publisher: IGI Global
Release Date : 2021-06-25

Handbook Of Research On Applied Data Science And Artificial Intelligence In Business And Industry written by Chkoniya, Valentina and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.