Model Optimization Techniques
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Model Optimization Methods for Efficient and Edge AI
Author: Pethuru Raj Chelliah
language: en
Publisher: John Wiley & Sons
Release Date: 2024-11-13
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.
Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems
Many factors can impact large-scale enterprise management systems, and maintaining these systems can be a complicated and challenging process. Therefore, businesses can benefit from an assortment of models and management styles to track and collect data for processes. Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems supplies a wide array of research on the intersections of business modeling, information systems, and optimization techniques. These various business models and structuring methods are proposed to provide ideas, methods, and points of view for managers, practitioners, entrepreneurs, and researchers on how to improve business processes.
Deep Learning Model Optimization, Deployment and Improvement Techniques for Edge-native Applications
Author: Pethuru Raj
language: en
Publisher: Cambridge Scholars Publishing
Release Date: 2024-08-22
The edge AI implementation technologies are fast maturing and stabilizing. Edge AI digitally transforms retail, manufacturing, healthcare, financial services, transportation, telecommunication, and energy. The transformative potential of Edge AI, a pivotal force in driving the evolution from Industry 4.0’s smart manufacturing and automation to Industry 5.0’s human-centric, sustainable innovation. The exploration of the cutting-edge technologies, tools, and applications that enable real-time data processing and intelligent decision-making at the network’s edge, addressing the increasing demand for efficiency, resilience, and personalization in industrial systems. Our book aims to provide readers with a comprehensive understanding of how Edge AI integrates with existing infrastructures, enhances operational capabilities, and fosters a symbiotic relationship between human expertise and machine intelligence. Through detailed case studies, technical insights, and practical guidelines, this book serves as an essential resource for professionals, researchers, and enthusiasts poised to harness the full potential of Edge AI in the rapidly advancing industrial landscape.