Scalable And Distributed Machine Learning And Deep Learning Patterns
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Scalable And Distributed Machine Learning And Deep Learning Patterns
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2023-08-25
Scalable And Distributed Machine Learning And Deep Learning Patterns written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with Computers categories.
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
Distributed Machine Learning Patterns
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Author : Yuan Tang
language : en
Publisher: Manning
Release Date : 2022-04-26
Distributed Machine Learning Patterns written by Yuan Tang and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-26 with Computers categories.
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you’ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Scalable Ai And Design Patterns
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Author : Abhishek Mishra
language : en
Publisher: Springer Nature
Release Date : 2024-03-11
Scalable Ai And Design Patterns written by Abhishek Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-11 with Computers categories.
Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will Learn Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing Know the significance of design patterns in creating robust intelligent solutions Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems Be aware of the ethical implications of scalable AI systems Who This Book Is For AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience
Designing Distributed Systems
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Author : Brendan Burns
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-02-20
Designing Distributed Systems written by Brendan Burns and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-20 with Computers categories.
Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot
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Author : Swarnalatha, P.
language : en
Publisher: IGI Global
Release Date : 2023-07-03
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot written by Swarnalatha, P. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Computers categories.
Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT demonstrates how the computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based internet of things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.
Deep Learning
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Author : Li Deng
language : en
Publisher:
Release Date : 2014
Deep Learning written by Li Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Machine learning categories.
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Deep Learning Innovations For Securing Critical Infrastructures
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Author : Kumar, Rajeev
language : en
Publisher: IGI Global
Release Date : 2025-04-18
Deep Learning Innovations For Securing Critical Infrastructures written by Kumar, Rajeev and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-18 with Computers categories.
Deep learning innovations play a crucial role in securing critical infrastructures, offering advanced solutions to protect vital systems from sophisticated cyber threats. By leveraging neural networks and advanced algorithms, deep learning enables real-time anomaly detection, pattern recognition, and predictive threat analysis, which are essential for safeguarding critical sectors such as energy, transportation, healthcare, and finance. These technologies can identify vulnerabilities, respond to breaches, and adapt to new attacks, providing a strong defense against cyber risks. As the digital landscape becomes more interconnected, the integration of deep learning into cybersecurity strategies will enhance resilience while ensuring the safe operation of essential services. Deep Learning Innovations for Securing Critical Infrastructures explores the cutting-edge integration of neural networks and artificial intelligence (AI) in modern cybersecurity systems. It examines how AI, particularly neural network models, is revolutionizing cybersecurity by automating threat detection, analyzing complex data patterns, and implementing proactive defense mechanisms. This book covers topics such as blockchain, cloud computing, and event management, and is a useful resource for business owners, computer engineers, data scientists, academicians, and researchers.
Battery Free Sensor Networks For Sustainable Next Generation Iot Connectivity
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Author : Karthick, G.S.
language : en
Publisher: IGI Global
Release Date : 2025-04-08
Battery Free Sensor Networks For Sustainable Next Generation Iot Connectivity written by Karthick, G.S. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Computers categories.
Battery-free sensor networks emerge as a pivotal technology for enabling sustainable, next-generation Internet of Things (IoT) connectivity. These networks leverage energy harvesting techniques, such as solar, thermal, or radiofrequency (RF) energy, to power sensors and devices, eliminating the need for traditional batteries. This reduces the environmental impact of battery disposal while extending the operational lifetime of IoT devices, making them more reliable and cost-effective. By harnessing energy sources, battery-free sensor networks hold the potential to revolutionize applications in smart cities, industrial monitoring, healthcare, and agriculture, contributing to the development of energy-efficient, self-sustaining IoT systems. Battery-Free Sensor Networks for Sustainable Next-Generation IoT Connectivity explores contemporary developments in battery-free sensor networks and their pivotal role in advancing sustainable connectivity within the next-generation IoT landscape. It delves into the latest advancements, challenges, and applications of battery-free sensor technologies, offering insights into their design principles, energy harvesting techniques, communication protocols, and deployment strategies. This book covers topics such as healthcare monitoring, sensor technology, and sustainability, and is a useful resource for engineers, scientists, environmentalists, business owners, academicians, researchers, and security professionals.