Modern Data Architecture in AI (notice n° 1556904)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03943cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88974249 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251020124311.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251020s2025 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789365899771 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88974249 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Choudhury, Abhik |
| 245 01 - TITLE STATEMENT | |
| Title | Modern Data Architecture in AI |
| Remainder of title | Optimize AI data storage, versioning, and partitioning with lakehouse (English Edition) |
| Statement of responsibility, etc. | ['Choudhury, Abhik', 'Puchakayala, Praneeth', 'Badlani, Aishwarya'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | DescriptionBuilding effective AI solutions demands a robust data architecture capable of handling vast, diverse, and real-time data. This book aims to provide a deep exploration of the tools, technologies, strategies, and best practices that necessitate the design, implementation, and management of data architectures tailored to AI.The book starts by introducing fundamental concepts of modern data architecture for AI, laying the groundwork for understanding its importance. It then digs deep into the aspects of data ingestion and collection strategies. Subsequently, it discusses data storage and management techniques that cater specifically to AI workloads. Readers will understand the concepts of data processing, transformation, and building scalable and efficient data pipelines, and how to orchestrate interconnected processes. The book further explores the topics of scalable ML infrastructure and stream processing, concluding with insights into visualization, explainable AI, and future trends.By the end of this book, the readers will have a comprehensive understanding and the skills to develop and manage scalable and efficient AI systems. They will have a firm grasp on the collection, storage, processing, and transformation of data, ensuring data governance and security. After reading this book, you will be well-equipped to design, build, and manage cutting-edge data architectures for diverse AI workloads, empowering your strategic initiatives.What you will learn? Build data pipelines with automated orchestration and monitoring.? Design scalable data lakes and lakehouse architectures for AI workloads.? Learn data governance, security, and compliance frameworks.? Leverage emerging technologies like quantum and edge computing.? Optimize infrastructure for distributed ML training and serving.? Visualize AI insights and apply explainable AI methods for transparency.? Understand LLMs, generative AI, federated learning, and their data architecture impact.? Architect real-time AI systems with online learning and low-latency stream processing.Who this book is forThis book is for data engineers, ML engineers, and enterprise architects who are at the forefront of designing and implementing scalable AI data systems. It is an essential guide for building robust data foundations. Software developers transitioning into AI infrastructure roles and technical leaders planning AI initiatives will also benefit significantly.Table of Contents1. Introduction to Modern Data Architecture for AI2. Data Collection and Ingestion Strategies3. Data Storage and Management for AI Workloads4. Data Processing and Transformation for AI5. Modern Data Pipeline Management6. Data Governance, Security, and Compliance in AI7. AI Algorithms and Their Impact on Data Architecture8. Scalable Machine Learning Infrastructure9. Real-time AI Systems and Stream Processing10. Data Visualization and Explainable AI11. Emerging Trends in AI Data Architecture |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Choudhury, Abhik |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Puchakayala, Praneeth |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Badlani, Aishwarya |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88974249">https://international.scholarvox.com/netsen/book/88974249</a> |
| Electronic format type | text/html |
| Host name | |
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