Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing

Authored by Rajiv Pandey, this comprehensive guide delves into the role of AI and machine learning in Edge Computing. It explores the impact of these technologies on various industries, including gaming, speech recognition, medical diagnostics, robotics, and computer vision, which are increasingly driven by Big Data, AI, machine learning, and distributed computing paradigms such as Cloud, Fog, and Edge Computing.

Challenges in Remote Storage and Computing

The book addresses the challenges associated with remote storage and computing, such as bandwidth overload due to data transportation from end nodes to Cloud, leading to latency issues. It also discusses security concerns related to transporting sensitive information across larger gaps in points of data generation and computing.

Design Features of Edge Nodes

The guide provides insights into the design features of Edge nodes, focusing on their ability to store and run AI/ML algorithms for effective rendering. It serves as a reference handbook on the evolution of distributed systems, including Cloud, Fog, and Edge Computing, and integrates various AI and machine learning techniques for predictions at the Edge rather than Cloud or remote Data Centers.

Technological and Architectural Developments

Furthermore, the book offers insights into the technological and architectural developments that are overcoming the constraints of Edge Computing and storage, including hardware limitations. It provides a comprehensive understanding of the features and constraints in Edge Computing and storage, making it an essential read for professionals and researchers in the field.