Distributed Machine Learning Patterns Av Yuan Tang

<div><div><b>Practical patterns for scaling machine learning from your laptop to a distributed cluster.</b><br><br>In  <span><b>Distributed Machine Learning Patterns</b></span> you will learn how to: <br><br><ul><li style="box-sizin borde

Practical patterns for scaling machine learning from your laptop to a distributed cluster. In Distributed Machine Learning Patternsyou will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Construct machine learning pipelines with data ingestion, distributed training, model serving, and more Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade offs between different patterns and approaches Manage 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 Patternsteaches 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.