Integrating Data
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Data-Driven Agentic AI: Integrating Data Science and Machine Learning
Data-Driven Agentic AI explores the emerging paradigm where autonomous agents interact with data, tools, and humans to solve complex problems across industries. Bridging the gap between data science, machine learning, and intelligent systems design, this book offers a detailed blueprint for building agentic AI that is autonomous, adaptive, and trustworthy. The book begins by grounding readers in the foundations of agency in artificial intelligence — defining key traits such as autonomy, goal orientation, and memory. It then builds into the architectural and technical elements required to create scalable and reliable agents, covering vector-based memory, tool integration, prompt orchestration, and multi-modal data pipelines. Key implementation frameworks like LangChain, AutoGen, and CrewAI are examined alongside infrastructure strategies for deploying agents in real-time, cloud-native environments. Extensive focus is placed on evaluation methodologies, debugging techniques, security, and compliance — equipping readers to monitor, align, and govern autonomous agents responsibly. Use cases span finance, healthcare, customer service, and robotics, demonstrating how agentic AI transforms industry practices. The final chapters explore collaborative human-agent interaction, ethical design, emergent behaviors, and decentralized multi-agent systems. A hands-on guide for practitioners concludes the book, detailing tools, workflows, and adoption roadmaps. Whether you're a data scientist, ML engineer, product leader, or researcher, this comprehensive guide delivers the theoretical grounding and practical insights to design and deploy intelligent, data-driven agents for the real world.
Integrating Data Science and Earth Science
This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
Principles of Data Integration
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications