Model Optimization Methods For Efficient And Edge Ai
Download Model Optimization Methods For Efficient And Edge Ai PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Model Optimization Methods For Efficient And Edge Ai book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Model Optimization Methods for Efficient and Edge AI
Author: Pethuru Raj Chelliah
language: en
Publisher: John Wiley & Sons
Release Date: 2025-01-09
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
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.
Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET)
As industries worldwide adopt advanced technologies and sustainable practices, the role of technical and vocational education and training (TVET) is evolving to meet these new demands. TVET institutions must now integrate artificial intelligence (AI) and sustainability into their programs to produce a workforce equipped with future-ready skills. By incorporating AI tools and sustainable practices into TVET curricula, educators can provide learners with the competencies to thrive in green technologies, smart manufacturing, renewable energy, and other emerging fields. This integration empowers individuals with new skills and contributes to a more sustainable, resilient global economy. Further exploration may bridge the gap between technological advancement and environmental responsibility. Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) provides a comprehensive guide on how TVET can successfully incorporate technological elements, addressing the frameworks, strategies, best practices, and challenges associated with this transformation. It supports educators in navigating the complexities of integrating AI and sustainability into vocational training. This book covers topics such as cybersecurity, data science, and supply chains, and is a useful resource for business owners, engineers, educators, academicians, researchers, and data scientists.