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Sinopsis
Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open‑source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real‑life experiences building AI and big data analytics systems of value to practitioners.
- Features practical case studies on building big data and AI models for large‑scale enterprise solutions
- Discusses the use of design patterns for architecting AI that are safe, secure, and testable
- Covers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programming
Informed by the authors’ many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Satish Mahadevan Srinivasan is an Associate Professor of Information Science at Pennsylvania State University, Great Valley. He teaches courses related to database design, data mining, data collection and cleaning, data visualization, computer, network and web securities, network analytics and business process management.
Raghvinder S. Sangwan is a Professor of Software Engineering at Pennsylvania State University with expertise in analysis, design, and development of large‑scale software‑intensive systems, and the use of AI engineering to design and develop intelligent systems that are safe, secure, and trustworthy.
"Sobre este título" puede pertenecer a otra edición de este libro.
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WHAT EVERY ENGINEER SHOULD KNOW ABOUT ARTIFICIAL INTELLIGENCE AND BIG DATA (PB 2027)
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What Every Engineer Should Know About Artificial Intelligence and Big Data
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Paperback. Condición: New. Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real-life experiences building AI and big data analytics systems of value to practitioners.Features practical case studies on building big data and AI models for large-scale enterprise solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors' many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies. Nº de ref. del artículo: LU-9781032829852
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Paperback. Condición: new. Paperback. Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed opensource computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares reallife experiences building AI and big data analytics systems of value to practitioners.Features practical case studies on building big data and AI models for largescale enterprise solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies. This book covers the essentials of big data and ML/AI to predict trends and risks for business while acknowledging that the field is extensive and evolving. Rather than focusing on theory, it shares real-life experiences building AI and big data analytics systems of value to practitioners. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032829852
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What Every Engineer Should Know About Artificial Intelligence and Big Data
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Taylor and Francis Ltd, GB, 2026
ISBN 10: 1032829850
ISBN 13: 9781032829852
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Paperback. Condición: New. Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real-life experiences building AI and big data analytics systems of value to practitioners.Features practical case studies on building big data and AI models for large-scale enterprise solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors' many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies. Nº de ref. del artículo: LU-9781032829852
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