Distributed Machine Learning Patterns (Paperback)

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

從筆記型電腦擴展到分散式集群的實用機器學習模式。

分散式機器學習系統使開發人員能夠處理跨多個集群的極大數據集，利用自動化工具並受益於硬件加速。本書揭示了擴展機器學習系統的挑戰的最佳實踐技巧和內部貼士。

在《分散式機器學習模式》中，您將學習如何：

• 應用分散式系統模式來構建可擴展和可靠的機器學習項目
• 使用數據輸入、分散式訓練、模型服務等構建機器學習流程
• 使用Kubernetes、TensorFlow、Kubeflow和Argo Workflows自動化機器學習任務
• 在不同模式和方法之間進行權衡
• 管理和監控大規模機器學習工作負載

在《分散式機器學習模式》中，您將學習如何將已建立的分散式系統模式應用於機器學習項目，並探索專為機器學習而創建的尖端新模式。本書以TensorFlow、Kubernetes、Kubeflow和Argo Workflows為基礎，以真實世界為根基，通過實例演示如何應用模式。通過實踐項目和清晰實用的DevOps技術，您可以輕鬆啟動、管理和監控基於雲的分散式機器學習流程。

購買印刷版書籍可獲得Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術

在現代分散式系統上部署機器學習應用程序將關注可靠性、性能、安全性和其他運營問題。在這本深入指南中，Argo和Kubeflow的項目負責人Yuan Tang分享了有關將機器學習模型從單一設備轉移到分散式集群的模式、示例和寶貴見解。

關於本書

《分散式機器學習模式》提供了設計和部署分散式機器學習系統的數十種技術。您將學習分散式模型訓練、處理意外故障和動態模型服務的模式。您將欣賞到每個模式附帶的實例以及一個實現在Kubernetes上自動縮放的分散式模型訓練和推理的完整項目。

內容簡介

• 數據輸入、分散式訓練、模型服務等
• 使用Kubeflow和Argo Workflows自動化Kubernetes和TensorFlow
• 管理和監控大規模工作負載

讀者對象

對於熟悉機器學習基礎知識、Bash、Python和Docker的數據分析師和工程師。

關於作者

Yuan Tang 是Argo和Kubeflow的項目負責人，TensorFlow和XGBoost的維護者，並且是許多開源項目的作者。

目錄

第1部分 基本概念和背景
1 分散式機器學習系統簡介
第2部分 分散式機器學習系統的模式
2 數據輸入模式
3 分散式訓練模式
4 模型服務模式
5 工作流模式
6 運營模式
第3部分 構建分散式機器學習工作流程
7 項目概述和系統架構
8 相關技術概述
9 完整實現

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.

Yuan Tang目前是Akuity的創始工程師。之前他在阿里巴巴集團擔任高級軟體工程師，負責在Kubernetes上建立AI基礎設施和AutoML平台。Yuan是Kubeflow的聯合主席，也是Argo、TensorFlow、XGBoost和Apache MXNet的維護者。他是《TensorFlow in Practice》的合著者，也是《Dive into Deep Learning》中TensorFlow實現的作者。