Synopsis
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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
"synopsis" may belong to another edition of this title.
About the Author
Yuan Tang is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.
From the Back Cover
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.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. Practical patterns for scaling machine learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projectsConstruct machine learning pipelines with data ingestion, distributed training, model serving, and moreAutomate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo WorkflowsMake trade offs between different patterns and approachesManage and monitor machine learning workloads at scale 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 Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, 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. about the technology Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure. about the book Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Re. Seller Inventory # LU-9781617299025
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