Download or read book Distributed Machine Learning Patterns written by Yuan Tang and published by Simon and Schuster. This book was released on 2024-01-30 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Download or read book Advances in Distributed Computing and Machine Learning written by Asis Kumar Tripathy and published by Springer Nature. This book was released on 2020-06-11 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in the field of distributed computing and machine learning, along with cutting-edge research in the field of Internet of Things (IoT) and blockchain in distributed environments. It features selected high-quality research papers from the First International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2020), organized by the School of Information Technology and Engineering, VIT, Vellore, India, and held on 30–31 January 2020.
Download or read book Distributed Machine Learning and Computing written by M. Hadi Amini and published by Springer. This book was released on 2024-06-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques.
Download or read book Distributed Algorithms written by Wan Fokkink and published by MIT Press. This book was released on 2013-12-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.
Download or read book Advances in Distributed Computing and Machine Learning written by Jyoti Prakash Sahoo and published by Springer Nature. This book was released on 2022-01-01 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in the field of scalable distributed computing including state-of-the-art research in the field of Cloud Computing, the Internet of Things (IoT), and Blockchain in distributed environments along with applications and findings in broad areas including Data Analytics, AI, and Machine Learning to address complex real-world problems. It features selected high-quality research papers from the 2nd International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2021), organized by the Department of Computer Science and Information Technology, Institute of Technical Education and Research(ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India.
Download or read book Scalable and Distributed Machine Learning and Deep Learning Patterns written by Thomas, J. Joshua and published by IGI Global. This book was released on 2023-08-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Coded Computing written by Songze Li and published by . This book was released on 2020 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce the concept of “coded computing”, a novel computing paradigm that utilizes coding theory to effectively inject and leverage data/computation redundancy to mitigate several fundamental bottlenecks in large-scale distributed computing, namely communication bandwidth, straggler’s (i.e., slow or failing nodes) delay, privacy and security bottlenecks.
Download or read book Distributed Computing and Artificial Intelligence 15th International Conference written by Fernando De La Prieta and published by Springer. This book was released on 2018-07-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 15th International Symposium on Distributed Computing and Artificial Intelligence 2018 (DCAI 2018) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the University of Castilla-La Mancha, the Osaka Institute of Technology and the University of Salamanca. The present edition was held in Toledo, Spain, from 20th – 22nd June, 2018.
Download or read book Robust Machine Learning written by Rachid Guerraoui and published by Springer Nature. This book was released on with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Distributed Services with Go written by Travis Jeffery and published by Pragmatic Bookshelf. This book was released on 2020-10-27 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: You know the basics of Go and are eager to put your knowledge to work. This book is just what you need to apply Go to real-world situations. You'll build a distributed service that's highly available, resilient, and scalable. Along the way you'll master the techniques, tools, and tricks that skilled Go programmers use every day to build quality applications. Level up your Go skills today. Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun. Lay out your applications and libraries to be modular and easy to maintain. Build networked, secure clients and servers with gRPC. Monitor your applications with metrics, logs, and traces to make them debuggable and reliable. Test and benchmark your applications to ensure they're correct and fast. Build your own distributed services with service discovery and consensus. Write CLIs to configure your applications. Deploy applications to the cloud with Kubernetes and manage them with your own Kubernetes Operator. Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world. What You Need: Go 1.11 and Kubernetes 1.12.
Download or read book Distributed Computing and Artificial Intelligence 13th International Conference written by Sigeru Omatu and published by Springer. This book was released on 2016-06-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13th International Symposium on Distributed Computing and Artificial Intelligence 2016 (DCAI 2016) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the University of Sevilla (Spain), Osaka Institute of Technology (Japan), and the Universiti Teknologi Malaysia (Malaysia)
Download or read book Advances in Distributed Computing and Machine Learning written by Umakanta Nanda and published by Springer. This book was released on 2024-06-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of peer-reviewed best selected research papers presented at the Fifth International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2024), organized by the School of Electronics and Engineering, VIT - AP University, Amaravati, Andhra Pradesh, India, during January 5–6, 2024. This book presents recent innovations in the field of scalable distributed systems in addition to cutting-edge research in the field of Internet of Things (IoT) and blockchain in distributed environments.
Download or read book Distributed Computing and Artificial Intelligence 14th International Conference written by Sigeru Omatu and published by Springer. This book was released on 2017-06-19 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 14th International Symposium on Distributed Computing and Artificial Intelligence 2017 (DCAI 2017) provided a forum for presenting the application of innovative techniques to study and solve complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to advancing the development of systems that can meet the ever-growing demands of today’s society. The book brings together past experience, current work and promising future trends in distributed computing, artificial intelligence and their applications to efficiently solve real-world problems. It combines contributions in well-established and evolving areas of research, including the content of the DCAI 17 Special Sessions, which focused on multi-disciplinary and transversal aspects, such as AI-driven methods for multimodal networks and processes modeling, and secure management towards smart buildings and smart grids. The symposium was jointly organized by the Polytechnic of Porto, the Osaka Institute of Technology and the University of Salamanca. The latest event was held in Porto, Portugal, from 21st to 23rd June 2017.
Download or read book Distributed Machine Learning and Computing written by M. Hadi Amini and published by Springer Nature. This book was released on with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fog Computing written by Assad Abbas and published by John Wiley & Sons. This book was released on 2020-04-21 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summarizes the current state and upcoming trends within the area of fog computing Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments. Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing Fog Computing: Theory and Practice provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.
Download or read book Distributed Computing and Intelligent Technology written by Raju Bapi and published by Springer Nature. This book was released on 2022-01-18 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 18th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2022, held in Bhubaneswar, India, in January 20212. The 11 full papers presented together with 4 short papers were carefully reviewed and selected from 50 submissions. There are also 4 invited papers included. The papers were organized in topical sections named: invited papers, distributed computing and intelligent technology.