Artificial Intelligence and Machine Learning for EDGE Computing
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Form:Einzelkauf Download
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Sprache:Englisch
148,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
26.04.2022
Herausgeber
Rajiv Pandey + weitereVerlag
Elsevier Science & Techn.Seitenzahl
516 (Printausgabe)
Dateigröße
93938 KB
Sprache
Englisch
EAN
9780128240557
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms.
Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering.
- Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing
- Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers
- Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering.
- Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing
- Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers
- Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
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