January 2024
Advanced
6h 21m
English
Overview
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
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.
Quotes
Approachable for beginners and inspirational for experienced practitioners. As soon as I finished reading, I was ready to start building.
- James Lamb, SpotHero
Exceptionally timely and comprehensive. Its pattern perspective, accompanied by real-world examples and widely adopted systems like Kubernetes, Kubeflow, and Argo, truly set it apart.
- Yuan Chen, Apple
An amazing guide to designing resilient and scalable ML systems for both training and serving models.
- Ryan Russon, Capital One
A wonderful book! Machine learning at scale explained clearly and from first principles!
- Laurence Moroney, Google
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