Название: Distributed Machine Learning Patterns (MEAP v5)
Автор: Yuan Tang
Издательство: Manning Publications
Год: 2022
Страниц: 172
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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 recent years, advances in Machine Learning have made tremendous progress yet large scale machine learning still remains challenging. With the variety of Machine Learning frameworks such as TensorFlow, PyTorch, and XGBoost, it’s not easy to automate the process of training Machine Learning models on distributed clusters. Machine Learning researchers and algorithm engineers with less or zero DevOps experience cannot easily launch, manage, and monitor distributed Machine Learning pipelines.

Being able to handle large scale problems and take what’s developed on your personal laptop to large distributed clusters is exciting. This book introduces key concepts and practical patterns commonly seen in good distributed Machine Learning systems. These patterns would greatly speed up the development and deployment of Machine Learning models, leverage automations from different tools, and benefit from hardware accelerations.

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.

There will be supplemental exercises at the end of each section in part two of the book to recap what we’ve learned. In addition, there will be one comprehensive hands-on project at the last part of the book that provides an opportunity to build a real-world distributed Machine Learning system that leverages many of the patterns we will learn in the second part of the book.