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.
Distributed Machine Learning Patterns
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Author : Yuan Tang
language : en
Publisher: Simon and Schuster
Release Date : 2024-01-30
Distributed Machine Learning Patterns written by Yuan Tang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.
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
Scaling Up Machine Learning
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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2012
Scaling Up Machine Learning written by Ron Bekkerman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Machine Learning With Apache Spark Quick Start Guide
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Author : Jillur Quddus
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26
Machine Learning With Apache Spark Quick Start Guide written by Jillur Quddus and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-26 with Computers categories.
Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.
Scalable Ai And Design Patterns
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Author : Abhishek Mishra
language : en
Publisher: Apress
Release Date : 2024-03-11
Scalable Ai And Design Patterns written by Abhishek Mishra and has been published by Apress 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
The Machine Learning Solutions Architect Handbook
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Author : David Ping
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-21
The Machine Learning Solutions Architect Handbook written by David Ping and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-21 with Computers categories.
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
Scaling Up Machine Learning
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Author : Ron Bekkerman
language : en
Publisher:
Release Date : 2012
Scaling Up Machine Learning written by Ron Bekkerman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Data mining categories.
"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"--
Deep Learning Patterns And Practices
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Author : Andrew Ferlitsch
language : en
Publisher: Simon and Schuster
Release Date : 2021-10-12
Deep Learning Patterns And Practices written by Andrew Ferlitsch and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-12 with Computers categories.
Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline
Machine Learning End To End Guide For Java Developers
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Author : Richard M. Reese
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-05
Machine Learning End To End Guide For Java Developers written by Richard M. Reese and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-05 with Computers categories.
Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming About This Book Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases Who This Book Is For This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. What You Will Learn Understand key data analysis techniques centered around machine learning Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more In Detail Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: Java for Data Science Machine Learning in Java Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence. Style and approach This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform.