Distributed machine learning patterns, yuan tang
Описание
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
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